Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity

• URL: http://arxiv.org/abs/2310.07521v3
• Date: Sat, 16 Dec 2023 12:47:19 GMT
• Title: Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
• Authors: Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang
• Abstract summary: This survey addresses the crucial issue of factuality in Large Language Models (LLMs) As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital.
• Score: 61.54815512469125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As LLMs find applications across diverse domains, the reliability and accuracy of their outputs become vital. We define the Factuality Issue as the probability of LLMs to produce content inconsistent with established facts. We first delve into the implications of these inaccuracies, highlighting the potential consequences and challenges posed by factual errors in LLM outputs. Subsequently, we analyze the mechanisms through which LLMs store and process facts, seeking the primary causes of factual errors. Our discussion then transitions to methodologies for evaluating LLM factuality, emphasizing key metrics, benchmarks, and studies. We further explore strategies for enhancing LLM factuality, including approaches tailored for specific domains. We focus two primary LLM configurations standalone LLMs and Retrieval-Augmented LLMs that utilizes external data, we detail their unique challenges and potential enhancements. Our survey offers a structured guide for researchers aiming to fortify the factual reliability of LLMs.

Related papers

• Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization [12.885866125783618]
  Large Language Models (LLMs) tend to produce inaccurate responses to specific queries. We construct an adversarial dataset, named as $textbfADT (Adrial dataset for Tokenizer)$ to challenge LLMs' tokenization. Our empirical results reveal that our ADT is highly effective on challenging the tokenization of leading LLMs, including GPT-4o, Llama-3, Qwen2.5-max and so on.
  arXiv  Detail & Related papers  (2024-05-27T11:39:59Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Benchmarking LLMs via Uncertainty Quantification [91.72588235407379]
  The proliferation of open-source Large Language Models (LLMs) has highlighted the urgent need for comprehensive evaluation methods. We introduce a new benchmarking approach for LLMs that integrates uncertainty quantification. Our findings reveal that: I) LLMs with higher accuracy may exhibit lower certainty; II) Larger-scale LLMs may display greater uncertainty compared to their smaller counterparts; and III) Instruction-finetuning tends to increase the uncertainty of LLMs.
  arXiv  Detail & Related papers  (2024-01-23T14:29:17Z)
• LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis [11.712916673150245]
  Large Language Models (LLMs) produce outputs that diverge from factual reality. This phenomenon is particularly concerning in sensitive applications such as medical consultation and legal advice. In this paper, we introduce the LLM factoscope, a novel Siamese network-based model that leverages the inner states of LLMs for factual detection.
  arXiv  Detail & Related papers  (2023-12-27T01:44:47Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Are Large Language Models Reliable Judges? A Study on the Factuality Evaluation Capabilities of LLMs [8.526956860672698]
  Large Language Models (LLMs) have gained immense attention due to their notable emergent capabilities. This study investigates the potential of LLMs as reliable assessors of factual consistency in summaries generated by text-generation models.
  arXiv  Detail & Related papers  (2023-11-01T17:42:45Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• Causal Reasoning and Large Language Models: Opening a New Frontier for Causality [29.433401785920065]
  Large language models (LLMs) can generate causal arguments with high probability. LLMs may be used by human domain experts to save effort in setting up a causal analysis.
  arXiv  Detail & Related papers  (2023-04-28T19:00:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.