Title stringlengths 16 196 | Authors stringlengths 6 6.27k | Abstract stringlengths 242 1.92k | entry_id stringlengths 33 33 | Date timestamp[ns, tz=UTC] | Categories stringclasses 597
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How faithful are RAG models? Quantifying the tug-of-war between RAG and LLMs' internal prior | Kevin Wu, Eric Wu, James Zou | Retrieval augmented generation (RAG) is often used to fix hallucinations and provide up-to-date knowledge for large language models (LLMs). However, in cases when the LLM alone incorrectly answers a question, does providing the correct retrieved content always fix the error? Conversely, in cases where the retrieved con... | http://arxiv.org/abs/2404.10198v1 | 2024-04-16T00:43:03Z | cs.CL, cs.AI | 2,024 |
Numerical Attributes Learning for Cardiac Failure Diagnostic from Clinical Narratives -- A LESA-CamemBERT-bio Approach | Boammani Aser Lompo, Thanh-Dung Le | Medical records created by healthcare professionals upon patient admission are rich in details critical for diagnosis. Yet, their potential is not fully realized because of obstacles such as complex medical language, inadequate comprehension of medical numerical data by state-of-the-art Large Language Models (LLMs), an... | http://arxiv.org/abs/2404.10171v1 | 2024-04-15T22:50:42Z | eess.SP | 2,024 |
RLRF:Reinforcement Learning from Reflection through Debates as Feedback for Bias Mitigation in LLMs | Ruoxi Cheng, Haoxuan Ma, Shuirong Cao, Tianyu Shi | Biases and stereotypes in Large Language Models (LLMs) can have negative implications for user experience and societal outcomes. Current approaches to bias mitigation like Reinforcement Learning from Human Feedback (RLHF) rely on costly manual feedback. While LLMs have the capability to understand logic and identify bi... | http://arxiv.org/abs/2404.10160v2 | 2024-04-15T22:18:50Z | cs.AI | 2,024 |
Quality Assessment of Prompts Used in Code Generation | Mohammed Latif Siddiq, Simantika Dristi, Joy Saha, Joanna C. S. Santos | Large Language Models (LLMs) are gaining popularity among software engineers. A crucial aspect of developing effective code-generation LLMs is to evaluate these models using a robust benchmark. Evaluation benchmarks with quality issues can provide a false sense of performance. In this work, we conduct the first-of-its-... | http://arxiv.org/abs/2404.10155v1 | 2024-04-15T22:02:58Z | cs.SE, cs.LG | 2,024 |
TabSQLify: Enhancing Reasoning Capabilities of LLMs Through Table Decomposition | Md Mahadi Hasan Nahid, Davood Rafiei | Table reasoning is a challenging task that requires understanding both natural language questions and structured tabular data. Large language models (LLMs) have shown impressive capabilities in natural language understanding and generation, but they often struggle with large tables due to their limited input length. In... | http://arxiv.org/abs/2404.10150v1 | 2024-04-15T21:42:20Z | cs.CL, cs.DB, cs.IR | 2,024 |
ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image Synthesis | Aashish Anantha Ramakrishnan, Sharon X. Huang, Dongwon Lee | Text-to-Image (T2I) Synthesis has made tremendous strides in enhancing synthesized image quality, but current datasets evaluate model performance only on descriptive, instruction-based prompts. Real-world news image captions take a more pragmatic approach, providing high-level situational and Named-Entity (NE) informat... | http://arxiv.org/abs/2404.10141v1 | 2024-04-15T21:19:10Z | cs.CV, cs.CL, cs.MM, 65D19 | 2,024 |
FEDSTR: Money-In AI-Out | A Decentralized Marketplace for Federated Learning and LLM Training on the NOSTR Protocol | Konstantinos E. Nikolakakis, George Chantzialexiou, Dionysis Kalogerias | The NOSTR is a communication protocol for the social web, based on the w3c websockets standard. Although it is still in its infancy, it is well known as a social media protocol, thousands of trusted users and multiple user interfaces, offering a unique experience and enormous capabilities. To name a few, the NOSTR appl... | http://arxiv.org/abs/2404.15834v1 | 2024-04-15T20:51:38Z | cs.DC, cs.AI, cs.CR, cs.LG | 2,024 |
LLM-based Test-driven Interactive Code Generation: User Study and Empirical Evaluation | Sarah Fakhoury, Aaditya Naik, Georgios Sakkas, Saikat Chakraborty, Shuvendu K. Lahiri | Large language models (LLMs) have shown great potential in automating significant aspects of coding by producing natural code from informal natural language (NL) intent. However, given NL is informal, it does not lend easily to checking that the generated code correctly satisfies the user intent. In this paper, we prop... | http://arxiv.org/abs/2404.10100v1 | 2024-04-15T19:16:32Z | cs.SE | 2,024 |
Group-wise Prompting for Synthetic Tabular Data Generation using Large Language Models | Jinhee Kim, Taesung Kim, Jaegul Choo | Generating realistic synthetic tabular data presents a critical challenge in machine learning. This study introduces a simple yet effective method employing Large Language Models (LLMs) tailored to generate synthetic data, specifically addressing data imbalance problems. We propose a novel group-wise prompting method i... | http://arxiv.org/abs/2404.12404v1 | 2024-04-15T17:49:16Z | cs.LG, cs.AI | 2,024 |
Constructing Benchmarks and Interventions for Combating Hallucinations in LLMs | Adi Simhi, Jonathan Herzig, Idan Szpektor, Yonatan Belinkov | Large language models (LLMs) are susceptible to hallucination, which sparked a widespread effort to detect and prevent them. Recent work attempts to mitigate hallucinations by intervening in the model's computation during generation, using different setups and heuristics. Those works lack separation between different h... | http://arxiv.org/abs/2404.09971v1 | 2024-04-15T17:48:46Z | cs.CL, I.2.7 | 2,024 |
Compression Represents Intelligence Linearly | Yuzhen Huang, Jinghan Zhang, Zifei Shan, Junxian He | There is a belief that learning to compress well will lead to intelligence. Recently, language modeling has been shown to be equivalent to compression, which offers a compelling rationale for the success of large language models (LLMs): the development of more advanced language models is essentially enhancing compressi... | http://arxiv.org/abs/2404.09937v1 | 2024-04-15T17:03:41Z | cs.CL, cs.AI, cs.IT, cs.LG, math.IT | 2,024 |
KG-CTG: Citation Generation through Knowledge Graph-guided Large Language Models | Avinash Anand, Mohit Gupta, Kritarth Prasad, Ujjwal Goel, Naman Lal, Astha Verma, Rajiv Ratn Shah | Citation Text Generation (CTG) is a task in natural language processing (NLP) that aims to produce text that accurately cites or references a cited document within a source document. In CTG, the generated text draws upon contextual cues from both the source document and the cited paper, ensuring accurate and relevant c... | http://arxiv.org/abs/2404.09763v1 | 2024-04-15T13:06:32Z | cs.CL, cs.AI | 2,024 |
Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model | Hyunsoo Cho | Many recent studies endeavor to improve open-source language models through imitation learning, and re-training on the synthetic instruction data from state-of-the-art proprietary models like ChatGPT and GPT-4. However, the innate nature of synthetic data inherently contains noisy data, giving rise to a substantial pre... | http://arxiv.org/abs/2404.09717v1 | 2024-04-15T12:20:09Z | cs.CL, cs.AI, cs.LG | 2,024 |
Are Large Language Models Reliable Argument Quality Annotators? | Nailia Mirzakhmedova, Marcel Gohsen, Chia Hao Chang, Benno Stein | Evaluating the quality of arguments is a crucial aspect of any system leveraging argument mining. However, it is a challenge to obtain reliable and consistent annotations regarding argument quality, as this usually requires domain-specific expertise of the annotators. Even among experts, the assessment of argument qual... | http://arxiv.org/abs/2404.09696v1 | 2024-04-15T11:54:27Z | cs.CL, cs.AI, cs.ET | 2,024 |
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation | Juhwan Choi, Jungmin Yun, Kyohoon Jin, YoungBin Kim | The quality of the dataset is crucial for ensuring optimal performance and reliability of downstream task models. However, datasets often contain noisy data inadvertently included during the construction process. Numerous attempts have been made to correct this issue through human annotators. However, hiring and managi... | http://arxiv.org/abs/2404.09682v1 | 2024-04-15T11:36:10Z | cs.CL, cs.AI | 2,024 |
Do LLMs Understand Visual Anomalies? Uncovering LLM Capabilities in Zero-shot Anomaly Detection | Jiaqi Zhu, Shaofeng Cai, Fang Deng, Junran Wu | Large vision-language models (LVLMs) are markedly proficient in deriving visual representations guided by natural language. Recent explorations have utilized LVLMs to tackle zero-shot visual anomaly detection (VAD) challenges by pairing images with textual descriptions indicative of normal and abnormal conditions, refe... | http://arxiv.org/abs/2404.09654v1 | 2024-04-15T10:42:22Z | cs.CV, cs.MM | 2,024 |
Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models | Siyan Zhao, Daniel Israel, Guy Van den Broeck, Aditya Grover | During inference for transformer-based large language models (LLM), prefilling is the computation of the key-value (KV) cache for input tokens in the prompt prior to autoregressive generation. For longer input prompt lengths, prefilling will incur a significant overhead on decoding time. In this work, we highlight the ... | http://arxiv.org/abs/2404.09529v1 | 2024-04-15T07:49:10Z | cs.LG, cs.AI, cs.CL | 2,024 |
LoongServe: Efficiently Serving Long-context Large Language Models with Elastic Sequence Parallelism | Bingyang Wu, Shengyu Liu, Yinmin Zhong, Peng Sun, Xuanzhe Liu, Xin Jin | The context window of large language models (LLMs) is rapidly increasing, leading to a huge variance in resource usage between different requests as well as between different phases of the same request. Restricted by static parallelism strategies, existing LLM serving systems cannot efficiently utilize the underlying r... | http://arxiv.org/abs/2404.09526v1 | 2024-04-15T07:45:04Z | cs.DC, cs.LG | 2,024 |
Large Language Models Can Automatically Engineer Features for Few-Shot Tabular Learning | Sungwon Han, Jinsung Yoon, Sercan O Arik, Tomas Pfister | Large Language Models (LLMs), with their remarkable ability to tackle challenging and unseen reasoning problems, hold immense potential for tabular learning, that is vital for many real-world applications. In this paper, we propose a novel in-context learning framework, FeatLLM, which employs LLMs as feature engineers ... | http://arxiv.org/abs/2404.09491v2 | 2024-04-15T06:26:08Z | cs.LG | 2,024 |
Entropy Guided Extrapolative Decoding to Improve Factuality in Large Language Models | Souvik Das, Lifeng Jin, Linfeng Song, Haitao Mi, Baolin Peng, Dong Yu | Large language models (LLMs) exhibit impressive natural language capabilities but suffer from hallucination -- generating content ungrounded in the realities of training data. Recent work has focused on decoding techniques to improve factuality during inference by leveraging LLMs' hierarchical representation of factual... | http://arxiv.org/abs/2404.09338v1 | 2024-04-14T19:45:35Z | cs.CL | 2,024 |
Self-Selected Attention Span for Accelerating Large Language Model Inference | Tian Jin, Wanzin Yazar, Zifei Xu, Sayeh Sharify, Xin Wang | Large language models (LLMs) can solve challenging tasks. However, their inference computation on modern GPUs is highly inefficient due to the increasing number of tokens they must attend to as they generate new ones. To address this inefficiency, we capitalize on LLMs' problem-solving capabilities to optimize their ow... | http://arxiv.org/abs/2404.09336v1 | 2024-04-14T19:36:04Z | cs.CL, cs.AI | 2,024 |
Large Language Models are as persuasive as humans, but how? About the cognitive effort and moral-emotional language of LLM arguments | Carlos Carrasco-Farre | Large Language Models (LLMs) are already as persuasive as humans. However, we know very little about how they do it. This paper investigates the persuasion strategies of LLMs, comparing them with human-generated arguments. Using a dataset of 1,251 participants in an experiment, we analyze the persuasion strategies of L... | http://arxiv.org/abs/2404.09329v2 | 2024-04-14T19:01:20Z | cs.CL | 2,024 |
JaFIn: Japanese Financial Instruction Dataset | Kota Tanabe, Masahiro Suzuki, Hiroki Sakaji, Itsuki Noda | We construct an instruction dataset for the large language model (LLM) in the Japanese finance domain. Domain adaptation of language models, including LLMs, is receiving more attention as language models become more popular. This study demonstrates the effectiveness of domain adaptation through instruction tuning. To a... | http://arxiv.org/abs/2404.09260v1 | 2024-04-14T14:01:53Z | cs.CL, cs.CE | 2,024 |
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning | Amani Namboori, Shivam Mangale, Andy Rosenbaum, Saleh Soltan | The emergence of Large Language Models (LLMs) with capabilities like In-Context Learning (ICL) has ushered in new possibilities for data generation across various domains while minimizing the need for extensive data collection and modeling techniques. Researchers have explored ways to use this generated synthetic data ... | http://arxiv.org/abs/2404.09163v1 | 2024-04-14T06:55:42Z | cs.CL, cs.AI | 2,024 |
From Bytes to Borsch: Fine-Tuning Gemma and Mistral for the Ukrainian Language Representation | Artur Kiulian, Anton Polishko, Mykola Khandoga, Oryna Chubych, Jack Connor, Raghav Ravishankar, Adarsh Shirawalmath | In the rapidly advancing field of AI and NLP, generative large language models (LLMs) stand at the forefront of innovation, showcasing unparalleled abilities in text understanding and generation. However, the limited representation of low-resource languages like Ukrainian poses a notable challenge, restricting the reac... | http://arxiv.org/abs/2404.09138v1 | 2024-04-14T04:25:41Z | cs.CL, cs.AI, cs.LG | 2,024 |
CuriousLLM: Elevating Multi-Document QA with Reasoning-Infused Knowledge Graph Prompting | Zukang Yang, Zixuan Zhu | In the field of Question Answering (QA), unifying large language models (LLMs) with external databases has shown great success. However, these methods often fall short in providing the advanced reasoning needed for complex QA tasks. To address these issues, we improve over a novel approach called Knowledge Graph Prompt... | http://arxiv.org/abs/2404.09077v1 | 2024-04-13T20:43:46Z | cs.CL, cs.AI, cs.IR, cs.LG | 2,024 |
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model | Zita Lifelo, Huansheng Ning, Sahraoui Dhelim | Timely identification is essential for the efficient handling of mental health illnesses such as depression. However, the current research fails to adequately address the prediction of mental health conditions from social media data in low-resource African languages like Swahili. This study introduces two distinct appr... | http://arxiv.org/abs/2404.09045v1 | 2024-04-13T17:11:35Z | cs.CL, cs.AI, cs.CY, cs.LG | 2,024 |
Intuition-aware Mixture-of-Rank-1-Experts for Parameter Efficient Finetuning | Yijiang Liu, Rongyu Zhang, Huanrui Yang, Kurt Keutzer, Yuan Du, Li Du, Shanghang Zhang | Large Language Models (LLMs) have demonstrated significant potential in performing multiple tasks in multimedia applications, ranging from content generation to interactive entertainment, and artistic creation. However, the diversity of downstream tasks in multitask scenarios presents substantial adaptation challenges ... | http://arxiv.org/abs/2404.08985v1 | 2024-04-13T12:14:58Z | cs.LG, cs.AI | 2,024 |
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM | Henry Peng Zou, Gavin Heqing Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, Cornelia Caragea | In e-commerce, accurately extracting product attribute values from multimodal data is crucial for improving user experience and operational efficiency of retailers. However, previous approaches to multimodal attribute value extraction often struggle with implicit attribute values embedded in images or text, rely heavil... | http://arxiv.org/abs/2404.08886v1 | 2024-04-13T03:15:56Z | cs.CV, cs.AI, cs.CL, cs.IR, cs.LG | 2,024 |
"Don't forget to put the milk back!" Dataset for Enabling Embodied Agents to Detect Anomalous Situations | James F. Mullen Jr, Prasoon Goyal, Robinson Piramuthu, Michael Johnston, Dinesh Manocha, Reza Ghanadan | Home robots intend to make their users lives easier. Our work assists in this goal by enabling robots to inform their users of dangerous or unsanitary anomalies in their home. Some examples of these anomalies include the user leaving their milk out, forgetting to turn off the stove, or leaving poison accessible to chil... | http://arxiv.org/abs/2404.08827v1 | 2024-04-12T21:56:21Z | cs.RO, cs.CV | 2,024 |
LLM-Seg: Bridging Image Segmentation and Large Language Model Reasoning | Junchi Wang, Lei Ke | Understanding human instructions to identify the target objects is vital for perception systems. In recent years, the advancements of Large Language Models (LLMs) have introduced new possibilities for image segmentation. In this work, we delve into reasoning segmentation, a novel task that enables segmentation system t... | http://arxiv.org/abs/2404.08767v1 | 2024-04-12T18:45:51Z | cs.CV, cs.LG | 2,024 |
VizGroup: An AI-Assisted Event-Driven System for Real-Time Collaborative Programming Learning Analytics | Xiaohang Tang, Sam Wong, Kevin Pu, Xi Chen, Yalong Yang, Yan Chen | Programming instructors often conduct collaborative learning activities, like Peer Instruction, to foster a deeper understanding in students and enhance their engagement with learning. These activities, however, may not always yield productive outcomes due to the diversity of student mental models and their ineffective... | http://arxiv.org/abs/2404.08743v1 | 2024-04-12T18:10:40Z | cs.HC | 2,024 |
Can LLMs substitute SQL? Comparing Resource Utilization of Querying LLMs versus Traditional Relational Databases | Xiang Zhang, Khatoon Khedri, Reza Rawassizadeh | Large Language Models (LLMs) can automate or substitute different types of tasks in the software engineering process. This study evaluates the resource utilization and accuracy of LLM in interpreting and executing natural language queries against traditional SQL within relational database management systems. We empiric... | http://arxiv.org/abs/2404.08727v1 | 2024-04-12T16:44:28Z | cs.DB, cs.AI, cs.CL, 68-04, H.2.m | 2,024 |
Small Models Are (Still) Effective Cross-Domain Argument Extractors | William Gantt, Aaron Steven White | Effective ontology transfer has been a major goal of recent work on event argument extraction (EAE). Two methods in particular -- question answering (QA) and template infilling (TI) -- have emerged as promising approaches to this problem. However, detailed explorations of these techniques' ability to actually enable th... | http://arxiv.org/abs/2404.08579v1 | 2024-04-12T16:23:41Z | cs.CL, cs.AI, cs.LG | 2,024 |
Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward | Xuan Xie, Jiayang Song, Zhehua Zhou, Yuheng Huang, Da Song, Lei Ma | While Large Language Models (LLMs) have seen widespread applications across numerous fields, their limited interpretability poses concerns regarding their safe operations from multiple aspects, e.g., truthfulness, robustness, and fairness. Recent research has started developing quality assurance methods for LLMs, intro... | http://arxiv.org/abs/2404.08517v1 | 2024-04-12T14:55:16Z | cs.SE, cs.AI, cs.CL, cs.CR, cs.LG | 2,024 |
Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction | Haoran Qiu, Weichao Mao, Archit Patke, Shengkun Cui, Saurabh Jha, Chen Wang, Hubertus Franke, Zbigniew T. Kalbarczyk, Tamer Başar, Ravishankar K. Iyer | Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains. However, efficiently serving LLM inference requests is challenging due to their unpredictable execution times originating from the autoregressive nature of generative models. Existing LLM serving systems ex... | http://arxiv.org/abs/2404.08509v1 | 2024-04-12T14:46:15Z | cs.DC, cs.CL, cs.LG | 2,024 |
Thematic Analysis with Large Language Models: does it work with languages other than English? A targeted test in Italian | Stefano De Paoli | This paper proposes a test to perform Thematic Analysis (TA) with Large Language Model (LLM) on data which is in a different language than English. While there has been initial promising work on using pre-trained LLMs for TA on data in English, we lack any tests on whether these models can reasonably perform the same a... | http://arxiv.org/abs/2404.08488v1 | 2024-04-12T14:10:09Z | cs.CL | 2,024 |
Pretraining and Updating Language- and Domain-specific Large Language Model: A Case Study in Japanese Business Domain | Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Tatsuya Ishigaki | Several previous studies have considered language- and domain-specific large language models (LLMs) as separate topics. This study explores the combination of a non-English language and a high-demand industry domain, focusing on a Japanese business-specific LLM. This type of a model requires expertise in the business d... | http://arxiv.org/abs/2404.08262v2 | 2024-04-12T06:21:48Z | cs.CL, cs.AI, 68T50 | 2,024 |
LLM Agents can Autonomously Exploit One-day Vulnerabilities | Richard Fang, Rohan Bindu, Akul Gupta, Daniel Kang | LLMs have becoming increasingly powerful, both in their benign and malicious uses. With the increase in capabilities, researchers have been increasingly interested in their ability to exploit cybersecurity vulnerabilities. In particular, recent work has conducted preliminary studies on the ability of LLM agents to auto... | http://arxiv.org/abs/2404.08144v2 | 2024-04-11T22:07:19Z | cs.CR, cs.AI | 2,024 |
Rumour Evaluation with Very Large Language Models | Dahlia Shehata, Robin Cohen, Charles Clarke | Conversational prompt-engineering-based large language models (LLMs) have enabled targeted control over the output creation, enhancing versatility, adaptability and adhoc retrieval. From another perspective, digital misinformation has reached alarming levels. The anonymity, availability and reach of social media offer ... | http://arxiv.org/abs/2404.16859v1 | 2024-04-11T19:38:22Z | cs.CL, cs.SI | 2,024 |
Data-Augmentation-Based Dialectal Adaptation for LLMs | Fahim Faisal, Antonios Anastasopoulos | This report presents GMUNLP's participation to the Dialect-Copa shared task at VarDial 2024, which focuses on evaluating the commonsense reasoning capabilities of large language models (LLMs) on South Slavic micro-dialects. The task aims to assess how well LLMs can handle non-standard dialectal varieties, as their perf... | http://arxiv.org/abs/2404.08092v1 | 2024-04-11T19:15:32Z | cs.CL, cs.AI | 2,024 |
SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions | Stefan Sylvius Wagner, Maike Behrendt, Marc Ziegele, Stefan Harmeling | Stance detection is an important task for many applications that analyse or support online political discussions. Common approaches include fine-tuning transformer based models. However, these models require a large amount of labelled data, which might not be available. In this work, we present two different ways to le... | http://arxiv.org/abs/2404.08078v1 | 2024-04-11T18:34:11Z | cs.CL, cs.AI, cs.LG | 2,024 |
MSciNLI: A Diverse Benchmark for Scientific Natural Language Inference | Mobashir Sadat, Cornelia Caragea | The task of scientific Natural Language Inference (NLI) involves predicting the semantic relation between two sentences extracted from research articles. This task was recently proposed along with a new dataset called SciNLI derived from papers published in the computational linguistics domain. In this paper, we aim to... | http://arxiv.org/abs/2404.08066v1 | 2024-04-11T18:12:12Z | cs.CL | 2,024 |
LLoCO: Learning Long Contexts Offline | Sijun Tan, Xiuyu Li, Shishir Patil, Ziyang Wu, Tianjun Zhang, Kurt Keutzer, Joseph E. Gonzalez, Raluca Ada Popa | Processing long contexts remains a challenge for large language models (LLMs) due to the quadratic computational and memory overhead of the self-attention mechanism and the substantial KV cache sizes during generation. We propose a novel approach to address this problem by learning contexts offline through context comp... | http://arxiv.org/abs/2404.07979v1 | 2024-04-11T17:57:22Z | cs.CL, cs.AI, cs.LG | 2,024 |
A Multi-Expert Large Language Model Architecture for Verilog Code Generation | Bardia Nadimi, Hao Zheng | Recently, there has been a surging interest in using large language models (LLMs) for Verilog code generation. However, the existing approaches are limited in terms of the quality of the generated Verilog code. To address such limitations, this paper introduces an innovative multi-expert LLM architecture for Verilog co... | http://arxiv.org/abs/2404.08029v1 | 2024-04-11T16:58:29Z | cs.LG, cs.AI, cs.PL, cs.SE | 2,024 |
The Future of Scientific Publishing: Automated Article Generation | Jeremy R. Harper | This study introduces a novel software tool leveraging large language model (LLM) prompts, designed to automate the generation of academic articles from Python code a significant advancement in the fields of biomedical informatics and computer science. Selected for its widespread adoption and analytical versatility, Py... | http://arxiv.org/abs/2404.17586v1 | 2024-04-11T16:47:02Z | cs.HC, cs.AI, cs.ET | 2,024 |
Post-Hoc Reversal: Are We Selecting Models Prematurely? | Rishabh Ranjan, Saurabh Garg, Mrigank Raman, Carlos Guestrin, Zachary Chase Lipton | Trained models are often composed with post-hoc transforms such as temperature scaling (TS), ensembling and stochastic weight averaging (SWA) to improve performance, robustness, uncertainty estimation, etc. However, such transforms are typically applied only after the base models have already been finalized by standard... | http://arxiv.org/abs/2404.07815v1 | 2024-04-11T14:58:19Z | cs.LG, cs.AI, stat.ML | 2,024 |
Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Models | Andreas Säuberli, Simon Clematide | Reading comprehension tests are used in a variety of applications, reaching from education to assessing the comprehensibility of simplified texts. However, creating such tests manually and ensuring their quality is difficult and time-consuming. In this paper, we explore how large language models (LLMs) can be used to g... | http://arxiv.org/abs/2404.07720v1 | 2024-04-11T13:11:21Z | cs.CL | 2,024 |
ODA: Observation-Driven Agent for integrating LLMs and Knowledge Graphs | Lei Sun, Zhengwei Tao, Youdi Li, Hiroshi Arakawa | The integration of Large Language Models (LLMs) and knowledge graphs (KGs) has achieved remarkable success in various natural language processing tasks. However, existing methodologies that integrate LLMs and KGs often navigate the task-solving process solely based on the LLM's analysis of the question, overlooking the... | http://arxiv.org/abs/2404.07677v1 | 2024-04-11T12:16:16Z | cs.CL, cs.AI | 2,024 |
Audio Dialogues: Dialogues dataset for audio and music understanding | Arushi Goel, Zhifeng Kong, Rafael Valle, Bryan Catanzaro | Existing datasets for audio understanding primarily focus on single-turn interactions (i.e. audio captioning, audio question answering) for describing audio in natural language, thus limiting understanding audio via interactive dialogue. To address this gap, we introduce Audio Dialogues: a multi-turn dialogue dataset c... | http://arxiv.org/abs/2404.07616v1 | 2024-04-11T10:08:34Z | cs.CL, cs.SD, eess.AS | 2,024 |
Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios? | Marcel Hallgarten, Julian Zapata, Martin Stoll, Katrin Renz, Andreas Zell | Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In particular, nuPlan seems to be an expressive evaluation method since it is... | http://arxiv.org/abs/2404.07569v1 | 2024-04-11T08:57:48Z | cs.RO, cs.AI, cs.LG | 2,024 |
From Words to Numbers: Your Large Language Model Is Secretly A Capable Regressor When Given In-Context Examples | Robert Vacareanu, Vlad-Andrei Negru, Vasile Suciu, Mihai Surdeanu | We analyze how well pre-trained large language models (e.g., Llama2, GPT-4, Claude 3, etc) can do linear and non-linear regression when given in-context examples, without any additional training or gradient updates. Our findings reveal that several large language models (e.g., GPT-4, Claude 3) are able to perform regre... | http://arxiv.org/abs/2404.07544v2 | 2024-04-11T08:12:43Z | cs.CL, cs.AI | 2,024 |
Introducing L2M3, A Multilingual Medical Large Language Model to Advance Health Equity in Low-Resource Regions | Agasthya Gangavarapu | Addressing the imminent shortfall of 10 million health workers by 2030, predominantly in Low- and Middle-Income Countries (LMICs), this paper introduces an innovative approach that harnesses the power of Large Language Models (LLMs) integrated with machine translation models. This solution is engineered to meet the uni... | http://arxiv.org/abs/2404.08705v1 | 2024-04-11T07:39:22Z | cs.CL, cs.AI, cs.LG | 2,024 |
MM-PhyQA: Multimodal Physics Question-Answering With Multi-Image CoT Prompting | Avinash Anand, Janak Kapuriya, Apoorv Singh, Jay Saraf, Naman Lal, Astha Verma, Rushali Gupta, Rajiv Shah | While Large Language Models (LLMs) can achieve human-level performance in various tasks, they continue to face challenges when it comes to effectively tackling multi-step physics reasoning tasks. To identify the shortcomings of existing models and facilitate further research in this area, we curated a novel dataset, MM... | http://arxiv.org/abs/2404.08704v1 | 2024-04-11T07:11:47Z | cs.CL, cs.AI | 2,024 |
Can Large Language Models Assess Serendipity in Recommender Systems? | Yu Tokutake, Kazushi Okamoto | Serendipity-oriented recommender systems aim to counteract over-specialization in user preferences. However, evaluating a user's serendipitous response towards a recommended item can be challenging because of its emotional nature. In this study, we address this issue by leveraging the rich knowledge of large language m... | http://arxiv.org/abs/2404.07499v1 | 2024-04-11T06:22:56Z | cs.IR | 2,024 |
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMs | Kanchana Ranasinghe, Satya Narayan Shukla, Omid Poursaeed, Michael S. Ryoo, Tsung-Yu Lin | Integration of Large Language Models (LLMs) into visual domain tasks, resulting in visual-LLMs (V-LLMs), has enabled exceptional performance in vision-language tasks, particularly for visual question answering (VQA). However, existing V-LLMs (e.g. BLIP-2, LLaVA) demonstrate weak spatial reasoning and localization aware... | http://arxiv.org/abs/2404.07449v1 | 2024-04-11T03:09:34Z | cs.CV | 2,024 |
JetMoE: Reaching Llama2 Performance with 0.1M Dollars | Yikang Shen, Zhen Guo, Tianle Cai, Zengyi Qin | Large Language Models (LLMs) have achieved remarkable results, but their increasing resource demand has become a major obstacle to the development of powerful and accessible super-human intelligence. This report introduces JetMoE-8B, a new LLM trained with less than $0.1 million, using 1.25T tokens from carefully mixed... | http://arxiv.org/abs/2404.07413v1 | 2024-04-11T00:52:39Z | cs.CL, cs.AI | 2,024 |
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving | Chenyang An, Zhibo Chen, Qihao Ye, Emily First, Letian Peng, Jiayun Zhang, Zihan Wang, Sorin Lerner, Jingbo Shang | Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i.e. proof steps) to search through proof states. The current model, while trained solely on successful proof paths, faces a discrepancy at the inference stage, as it must sample and ... | http://arxiv.org/abs/2404.07382v1 | 2024-04-10T23:01:45Z | cs.AI, cs.LO | 2,024 |
Analyzing the Performance of Large Language Models on Code Summarization | Rajarshi Haldar, Julia Hockenmaier | Large language models (LLMs) such as Llama 2 perform very well on tasks that involve both natural language and source code, particularly code summarization and code generation. We show that for the task of code summarization, the performance of these models on individual examples often depends on the amount of (subword... | http://arxiv.org/abs/2404.08018v1 | 2024-04-10T22:42:18Z | cs.SE, cs.AI, cs.CL | 2,024 |
LLMs in Biomedicine: A study on clinical Named Entity Recognition | Masoud Monajatipoor, Jiaxin Yang, Joel Stremmel, Melika Emami, Fazlolah Mohaghegh, Mozhdeh Rouhsedaghat, Kai-Wei Chang | Large Language Models (LLMs) demonstrate remarkable versatility in various NLP tasks but encounter distinct challenges in biomedicine due to medical language complexities and data scarcity. This paper investigates the application of LLMs in the medical domain by exploring strategies to enhance their performance for the... | http://arxiv.org/abs/2404.07376v1 | 2024-04-10T22:26:26Z | cs.CL | 2,024 |
Is Your LLM Outdated? Benchmarking LLMs & Alignment Algorithms for Time-Sensitive Knowledge | Seyed Mahed Mousavi, Simone Alghisi, Giuseppe Riccardi | We study the appropriateness of Large Language Models (LLMs) as knowledge repositories. We focus on the challenge of maintaining LLMs' factual knowledge up-to-date over time. Motivated by the lack of studies on identifying outdated knowledge within LLMs, we design and develop a dynamic benchmark with up-to-date ground ... | http://arxiv.org/abs/2404.08700v1 | 2024-04-10T18:08:59Z | cs.CL, cs.AI | 2,024 |
Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in LLMs | Ahmed Agiza, Mohamed Mostagir, Sherief Reda | In an era where language models are increasingly integrated into decision-making and communication, understanding the biases within Large Language Models (LLMs) becomes imperative, especially when these models are applied in the economic and political domains. This work investigates the impact of fine-tuning and data s... | http://arxiv.org/abs/2404.08699v2 | 2024-04-10T16:30:09Z | cs.CL, cs.AI, cs.LG | 2,024 |
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs | Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Suhang Wang, Yu Meng, Jiawei Han | Large language models (LLMs), while exhibiting exceptional performance, suffer from hallucinations, especially on knowledge-intensive tasks. Existing works propose to augment LLMs with individual text units retrieved from external knowledge corpora to alleviate the issue. However, in many domains, texts are interconnec... | http://arxiv.org/abs/2404.07103v1 | 2024-04-10T15:41:53Z | cs.CL, cs.IR, cs.LG | 2,024 |
Dynamic Generation of Personalities with Large Language Models | Jianzhi Liu, Hexiang Gu, Tianyu Zheng, Liuyu Xiang, Huijia Wu, Jie Fu, Zhaofeng He | In the realm of mimicking human deliberation, large language models (LLMs) show promising performance, thereby amplifying the importance of this research area. Deliberation is influenced by both logic and personality. However, previous studies predominantly focused on the logic of LLMs, neglecting the exploration of pe... | http://arxiv.org/abs/2404.07084v1 | 2024-04-10T15:17:17Z | cs.CL, cs.AI | 2,024 |
Exploring Concept Depth: How Large Language Models Acquire Knowledge at Different Layers? | Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang | Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities remain poorly understood. In this paper, we explore the hypothesis that LLMs process concepts of varying complexities in different layers, intr... | http://arxiv.org/abs/2404.07066v2 | 2024-04-10T14:56:40Z | cs.CL, cs.AI, cs.LG | 2,024 |
Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study | Alessandro Stolfo | We present an empirical study of groundedness in long-form question answering (LFQA) by retrieval-augmented large language models (LLMs). In particular, we evaluate whether every generated sentence is grounded in the retrieved documents or the model's pre-training data. Across 3 datasets and 4 model families, our findi... | http://arxiv.org/abs/2404.07060v1 | 2024-04-10T14:50:10Z | cs.CL, cs.LG | 2,024 |
Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models | Linan Yue, Qi Liu, Lili Zhao, Li Wang, Weibo Gao, Yanqing An | With the development of legal intelligence, Criminal Court View Generation has attracted much attention as a crucial task of legal intelligence, which aims to generate concise and coherent texts that summarize case facts and provide explanations for verdicts. Existing researches explore the key information in case fact... | http://arxiv.org/abs/2404.07001v3 | 2024-04-10T13:31:07Z | cs.CL, cs.AI | 2,024 |
XNLIeu: a dataset for cross-lingual NLI in Basque | Maite Heredia, Julen Etxaniz, Muitze Zulaika, Xabier Saralegi, Jeremy Barnes, Aitor Soroa | XNLI is a popular Natural Language Inference (NLI) benchmark widely used to evaluate cross-lingual Natural Language Understanding (NLU) capabilities across languages. In this paper, we expand XNLI to include Basque, a low-resource language that can greatly benefit from transfer-learning approaches. The new dataset, dub... | http://arxiv.org/abs/2404.06996v1 | 2024-04-10T13:19:56Z | cs.CL, cs.AI | 2,024 |
Quati: A Brazilian Portuguese Information Retrieval Dataset from Native Speakers | Mirelle Bueno, Eduardo Seiti de Oliveira, Rodrigo Nogueira, Roberto A. Lotufo, Jayr Alencar Pereira | Despite Portuguese being one of the most spoken languages in the world, there is a lack of high-quality information retrieval datasets in that language. We present Quati, a dataset specifically designed for the Brazilian Portuguese language. It comprises a collection of queries formulated by native speakers and a curat... | http://arxiv.org/abs/2404.06976v1 | 2024-04-10T12:42:28Z | cs.IR | 2,024 |
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation | Thomas Merth, Qichen Fu, Mohammad Rastegari, Mahyar Najibi | Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts. Their inference cost scales quadratically with respect to sequence length, making it expensive for deployment in some real-world text processing applications, such as retrieval-augmente... | http://arxiv.org/abs/2404.06910v1 | 2024-04-10T11:03:17Z | cs.CL, cs.AI, cs.LG | 2,024 |
Enhancing Question Answering for Enterprise Knowledge Bases using Large Language Models | Feihu Jiang, Chuan Qin, Kaichun Yao, Chuyu Fang, Fuzhen Zhuang, Hengshu Zhu, Hui Xiong | Efficient knowledge management plays a pivotal role in augmenting both the operational efficiency and the innovative capacity of businesses and organizations. By indexing knowledge through vectorization, a variety of knowledge retrieval methods have emerged, significantly enhancing the efficacy of knowledge management ... | http://arxiv.org/abs/2404.08695v2 | 2024-04-10T10:38:17Z | cs.CL, cs.AI, cs.IR | 2,024 |
Simpler becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora? | Miriam Anschütz, Edoardo Mosca, Georg Groh | Text simplification seeks to improve readability while retaining the original content and meaning. Our study investigates whether pre-trained classifiers also maintain such coherence by comparing their predictions on both original and simplified inputs. We conduct experiments using 11 pre-trained models, including BERT... | http://arxiv.org/abs/2404.06838v1 | 2024-04-10T09:02:33Z | cs.CL | 2,024 |
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge | Li Zhou, Taelin Karidi, Nicolas Garneau, Yong Cao, Wanlong Liu, Wenyu Chen, Daniel Hershcovich | Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively. Our work aims to bridge this gap by delving into the Food domain, a universally relevant yet culturally diverse aspect of human life. We introd... | http://arxiv.org/abs/2404.06833v1 | 2024-04-10T08:49:27Z | cs.CL | 2,024 |
Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking | Xiaokang Zhang, Zijun Yao, Jing Zhang, Kaifeng Yun, Jifan Yu, Juanzi Li, Jie Tang | Detecting non-factual content is a longstanding goal to increase the trustworthiness of large language models (LLMs) generations. Current factuality probes, trained using humanannotated labels, exhibit limited transferability to out-of-distribution content, while online selfconsistency checking imposes extensive comput... | http://arxiv.org/abs/2404.06742v1 | 2024-04-10T05:00:35Z | cs.CL | 2,024 |
Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness | Xincan Feng, Akifumi Yoshimoto | Recent advancements in Natural Language Processing (NLP) have seen Large-scale Language Models (LLMs) excel at producing high-quality text for various purposes. Notably, in Text-To-Speech (TTS) systems, the integration of BERT for semantic token generation has underscored the importance of semantic content in producing... | http://arxiv.org/abs/2404.06714v3 | 2024-04-10T03:46:03Z | cs.CL, cs.SD, eess.AS | 2,024 |
Onco-Retriever: Generative Classifier for Retrieval of EHR Records in Oncology | Shashi Kant Gupta, Aditya Basu, Bradley Taylor, Anai Kothari, Hrituraj Singh | Retrieving information from EHR systems is essential for answering specific questions about patient journeys and improving the delivery of clinical care. Despite this fact, most EHR systems still rely on keyword-based searches. With the advent of generative large language models (LLMs), retrieving information can lead ... | http://arxiv.org/abs/2404.06680v1 | 2024-04-10T02:02:34Z | cs.CL | 2,024 |
CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge | Yu Ying Chiu, Liwei Jiang, Maria Antoniak, Chan Young Park, Shuyue Stella Li, Mehar Bhatia, Sahithya Ravi, Yulia Tsvetkov, Vered Shwartz, Yejin Choi | Frontier large language models (LLMs) are developed by researchers and practitioners with skewed cultural backgrounds and on datasets with skewed sources. However, LLMs' (lack of) multicultural knowledge cannot be effectively assessed with current methods for developing benchmarks. Existing multicultural evaluations pr... | http://arxiv.org/abs/2404.06664v1 | 2024-04-10T00:25:09Z | cs.CL, cs.AI, cs.HC | 2,024 |
Perplexed: Understanding When Large Language Models are Confused | Nathan Cooper, Torsten Scholak | Large Language Models (LLMs) have become dominant in the Natural Language Processing (NLP) field causing a huge surge in progress in a short amount of time. However, their limitations are still a mystery and have primarily been explored through tailored datasets to analyze a specific human-level skill such as negation,... | http://arxiv.org/abs/2404.06634v1 | 2024-04-09T22:03:39Z | cs.SE | 2,024 |
Sandwich attack: Multi-language Mixture Adaptive Attack on LLMs | Bibek Upadhayay, Vahid Behzadan | Large Language Models (LLMs) are increasingly being developed and applied, but their widespread use faces challenges. These include aligning LLMs' responses with human values to prevent harmful outputs, which is addressed through safety training methods. Even so, bad actors and malicious users have succeeded in attempt... | http://arxiv.org/abs/2404.07242v1 | 2024-04-09T18:29:42Z | cs.CR, cs.AI, cs.CL | 2,024 |
Pitfalls of Conversational LLMs on News Debiasing | Ipek Baris Schlicht, Defne Altiok, Maryanne Taouk, Lucie Flek | This paper addresses debiasing in news editing and evaluates the effectiveness of conversational Large Language Models in this task. We designed an evaluation checklist tailored to news editors' perspectives, obtained generated texts from three popular conversational models using a subset of a publicly available datase... | http://arxiv.org/abs/2404.06488v1 | 2024-04-09T17:42:59Z | cs.CL, cs.AI | 2,024 |
Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks | Chonghua Wang, Haodong Duan, Songyang Zhang, Dahua Lin, Kai Chen | Recently, the large language model (LLM) community has shown increasing interest in enhancing LLMs' capability to handle extremely long documents. As various long-text techniques and model architectures emerge, the precise and detailed evaluation of models' long-text capabilities has become increasingly important. Exis... | http://arxiv.org/abs/2404.06480v2 | 2024-04-09T17:30:48Z | cs.CL, cs.AI | 2,024 |
Rethinking How to Evaluate Language Model Jailbreak | Hongyu Cai, Arjun Arunasalam, Leo Y. Lin, Antonio Bianchi, Z. Berkay Celik | Large language models (LLMs) have become increasingly integrated with various applications. To ensure that LLMs do not generate unsafe responses, they are aligned with safeguards that specify what content is restricted. However, such alignment can be bypassed to produce prohibited content using a technique commonly ref... | http://arxiv.org/abs/2404.06407v3 | 2024-04-09T15:54:16Z | cs.CL, cs.AI, cs.CR, cs.LG | 2,024 |
Latent Distance Guided Alignment Training for Large Language Models | Haotian Luo | Ensuring alignment with human preferences is a crucial characteristic of large language models (LLMs). Presently, the primary alignment methods, RLHF and DPO, require extensive human annotation, which is expensive despite their efficacy. The significant expenses associated with current alignment techniques motivate res... | http://arxiv.org/abs/2404.06390v2 | 2024-04-09T15:33:09Z | cs.CL | 2,024 |
GUIDE: Graphical User Interface Data for Execution | Rajat Chawla, Adarsh Jha, Muskaan Kumar, Mukunda NS, Ishaan Bhola | In this paper, we introduce GUIDE, a novel dataset tailored for the advancement of Multimodal Large Language Model (MLLM) applications, particularly focusing on Robotic Process Automation (RPA) use cases. Our dataset encompasses diverse data from various websites including Apollo(62.67\%), Gmail(3.43\%), Calendar(10.98... | http://arxiv.org/abs/2404.16048v1 | 2024-04-09T11:59:41Z | cs.HC, cs.AI | 2,024 |
Low-Cost Generation and Evaluation of Dictionary Example Sentences | Bill Cai, Clarence Boon Liang Ng, Daniel Tan, Shelvia Hotama | Dictionary example sentences play an important role in illustrating word definitions and usage, but manually creating quality sentences is challenging. Prior works have demonstrated that language models can be trained to generate example sentences. However, they relied on costly customized models and word sense dataset... | http://arxiv.org/abs/2404.06224v1 | 2024-04-09T11:26:59Z | cs.CL, cs.AI, cs.LG | 2,024 |
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models | Sebastian Bordt, Harsha Nori, Vanessa Rodrigues, Besmira Nushi, Rich Caruana | While many have shown how Large Language Models (LLMs) can be applied to a diverse set of tasks, the critical issues of data contamination and memorization are often glossed over. In this work, we address this concern for tabular data. Specifically, we introduce a variety of different techniques to assess whether a lan... | http://arxiv.org/abs/2404.06209v1 | 2024-04-09T10:58:21Z | cs.LG, cs.AI, cs.CL | 2,024 |
Exploring the Potential of Large Foundation Models for Open-Vocabulary HOI Detection | Ting Lei, Shaofeng Yin, Yang Liu | Open-vocabulary human-object interaction (HOI) detection, which is concerned with the problem of detecting novel HOIs guided by natural language, is crucial for understanding human-centric scenes. However, prior zero-shot HOI detectors often employ the same levels of feature maps to model HOIs with varying distances, l... | http://arxiv.org/abs/2404.06194v2 | 2024-04-09T10:27:22Z | cs.CV | 2,024 |
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles | Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali, Marco Maggini, Marco Gori, Leonardo Rigutini | Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues that distinguish them from traditional crossword puzzles. Despite there exist several publicly available clue-answer pair databases for tradi... | http://arxiv.org/abs/2404.06186v1 | 2024-04-09T10:12:34Z | cs.CL, cs.AI | 2,024 |
FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models | Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang | The rapid development of large language model (LLM) evaluation methodologies and datasets has led to a profound challenge: integrating state-of-the-art evaluation techniques cost-effectively while ensuring reliability, reproducibility, and efficiency. Currently, there is a notable absence of a unified and adaptable fra... | http://arxiv.org/abs/2404.06003v1 | 2024-04-09T04:17:51Z | cs.CL, cs.AI | 2,024 |
AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts | Shaona Ghosh, Prasoon Varshney, Erick Galinkin, Christopher Parisien | As Large Language Models (LLMs) and generative AI become more widespread, the content safety risks associated with their use also increase. We find a notable deficiency in high-quality content safety datasets and benchmarks that comprehensively cover a wide range of critical safety areas. To address this, we define a b... | http://arxiv.org/abs/2404.05993v1 | 2024-04-09T03:54:28Z | cs.LG, cs.CL, cs.CY | 2,024 |
Event-enhanced Retrieval in Real-time Search | Yanan Zhang, Xiaoling Bai, Tianhua Zhou | The embedding-based retrieval (EBR) approach is widely used in mainstream search engine retrieval systems and is crucial in recent retrieval-augmented methods for eliminating LLM illusions. However, existing EBR models often face the "semantic drift" problem and insufficient focus on key information, leading to a low a... | http://arxiv.org/abs/2404.05989v1 | 2024-04-09T03:47:48Z | cs.CL, cs.IR | 2,024 |
Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation | Alireza Salemi, Surya Kallumadi, Hamed Zamani | This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to large language models fo... | http://arxiv.org/abs/2404.05970v1 | 2024-04-09T02:58:05Z | cs.CL, cs.IR | 2,024 |
Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models | Sowmya S. Sundaram, Benjamin Solomon, Avani Khatri, Anisha Laumas, Purvesh Khatri, Mark A. Musen | Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and reusability of datasets. This paper investigates the potential of large language models (LLMs), specifically GPT-4, to improve adherence to metadata standards. We conducted experiments on 200 random data records describing hu... | http://arxiv.org/abs/2404.05893v2 | 2024-04-08T22:29:53Z | cs.AI, cs.CL, cs.IR | 2,024 |
Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning | Ruiqi Zhang, Licong Lin, Yu Bai, Song Mei | Large Language Models (LLMs) often memorize sensitive, private, or copyrighted data during pre-training. LLM unlearning aims to eliminate the influence of undesirable data from the pre-trained model while preserving the model's utilities on other tasks. Several practical methods have recently been proposed for LLM unle... | http://arxiv.org/abs/2404.05868v1 | 2024-04-08T21:05:42Z | cs.LG, cs.AI, cs.CL, stat.ML | 2,024 |
GeniL: A Multilingual Dataset on Generalizing Language | Aida Mostafazadeh Davani, Sagar Gubbi, Sunipa Dev, Shachi Dave, Vinodkumar Prabhakaran | LLMs are increasingly transforming our digital ecosystem, but they often inherit societal biases learned from their training data, for instance stereotypes associating certain attributes with specific identity groups. While whether and how these biases are mitigated may depend on the specific use cases, being able to e... | http://arxiv.org/abs/2404.05866v1 | 2024-04-08T20:58:06Z | cs.CL | 2,024 |
LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding | Mingrui Wu, Sheng Cao | Recently embedding-based retrieval or dense retrieval have shown state of the art results, compared with traditional sparse or bag-of-words based approaches. This paper introduces a model-agnostic doc-level embedding framework through large language model (LLM) augmentation. In addition, it also improves some important... | http://arxiv.org/abs/2404.05825v1 | 2024-04-08T19:29:07Z | cs.IR, cs.AI | 2,024 |
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding | Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, Ser-Nam Lim | With the success of large language models (LLMs), integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However, existing LLM-based large multimodal models (e.g., Video-LLaMA, VideoChat) can only take in a limited number of frames for short video under... | http://arxiv.org/abs/2404.05726v2 | 2024-04-08T17:59:24Z | cs.CV | 2,024 |
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs | Keen You, Haotian Zhang, Eldon Schoop, Floris Weers, Amanda Swearngin, Jeffrey Nichols, Yinfei Yang, Zhe Gan | Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile U... | http://arxiv.org/abs/2404.05719v1 | 2024-04-08T17:55:44Z | cs.CV, cs.CL, cs.HC | 2,024 |
Evaluating Mathematical Reasoning Beyond Accuracy | Shijie Xia, Xuefeng Li, Yixin Liu, Tongshuang Wu, Pengfei Liu | The leaderboard of Large Language Models (LLMs) in mathematical tasks has been continuously updated. However, the majority of evaluations focus solely on the final results, neglecting the quality of the intermediate steps. This oversight can mask underlying problems, such as logical errors or unnecessary steps in the r... | http://arxiv.org/abs/2404.05692v1 | 2024-04-08T17:18:04Z | cs.CL | 2,024 |
Retrieval-Augmented Open-Vocabulary Object Detection | Jooyeon Kim, Eulrang Cho, Sehyung Kim, Hyunwoo J. Kim | Open-vocabulary object detection (OVD) has been studied with Vision-Language Models (VLMs) to detect novel objects beyond the pre-trained categories. Previous approaches improve the generalization ability to expand the knowledge of the detector, using 'positive' pseudo-labels with additional 'class' names, e.g., sock, ... | http://arxiv.org/abs/2404.05687v1 | 2024-04-08T17:10:45Z | cs.CV | 2,024 |
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