Improving Model Factuality with Fine-grained Critique-based Evaluator

• URL: http://arxiv.org/abs/2410.18359v2
• Date: Fri, 21 Mar 2025 19:57:02 GMT
• Title: Improving Model Factuality with Fine-grained Critique-based Evaluator
• Authors: Yiqing Xie, Wenxuan Zhou, Pradyot Prakash, Di Jin, Yuning Mao, Quintin Fettes, Arya Talebzadeh, Sinong Wang, Han Fang, Carolyn Rose, Daniel Fried, Hejia Zhang,
• Abstract summary: We train a factuality evaluator, FenCE, that provides LM generators with claim-level factuality feedback.<n>We present a framework that leverages FenCE to improve the factuality of LM generators by constructing training data.<n>Experiments show that our data augmentation methods improve the evaluator's accuracy by 2.9% on LLM-AggreFact.
• Score: 47.36934130646514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Factuality evaluation aims to detect factual errors produced by language models (LMs) and hence guide the development of more factual models. Towards this goal, we train a factuality evaluator, FenCE, that provides LM generators with claim-level factuality feedback. We conduct data augmentation on a combination of public judgment datasets to train FenCE to (1) generate textual critiques along with scores and (2) make claim-level judgment based on diverse source documents obtained by various tools. We then present a framework that leverages FenCE to improve the factuality of LM generators by constructing training data. Specifically, we generate a set of candidate responses, leverage FenCE to revise and score each response without introducing lesser-known facts, and train the generator by preferring highly scored revised responses. Experiments show that our data augmentation methods improve the evaluator's accuracy by 2.9% on LLM-AggreFact. With FenCE, we improve Llama2-7B-chat and Llama3-8B-chat's factuality rate by 16.86% and 14.45% on FActScore, outperforming state-of-the-art factuality finetuning methods by 8.83% and 6.96%.

Related papers

• R-PRM: Reasoning-Driven Process Reward Modeling [53.06844294668382]
  Process Reward Models (PRMs) have emerged as a promising solution by evaluating each reasoning step. Existing PRMs typically output evaluation scores directly, limiting both learning efficiency and evaluation accuracy. We propose Reasoning-Driven Process Reward Modeling (R-PRM) R-PRM generates seed data from limited annotations, effectively bootstrapping our model's reasoning capabilities.
  arXiv  Detail & Related papers  (2025-03-27T09:23:08Z)
• Bayesian Calibration of Win Rate Estimation with LLM Evaluators [20.588104799661014]
  We propose two methods to improve the accuracy of win rate estimation using large language models (LLMs) as evaluators. We empirically validate our methods on six datasets covering story generation, summarization, and instruction following tasks.
  arXiv  Detail & Related papers  (2024-11-07T04:32:40Z)
• How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models [95.44559524735308]
  Large language or multimodal model based verification has been proposed to scale up online policing mechanisms for mitigating spread of false and harmful content. We test the limits of improving foundation model performance without continual updating through an initial study of knowledge transfer. Our results on two recent multi-modal fact-checking benchmarks, Mocheg and Fakeddit, indicate that knowledge transfer strategies can improve Fakeddit performance over the state-of-the-art by up to 1.7% and Mocheg performance by up to 2.9%.
  arXiv  Detail & Related papers  (2024-06-29T08:39:07Z)
• CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation [87.44350003888646]
  Eval-Instruct can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting. CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines.
  arXiv  Detail & Related papers  (2023-11-30T16:52:42Z)
• Fine-tuning Language Models for Factuality [96.5203774943198]
  Large pre-trained language models (LLMs) have led to their widespread use, sometimes even as a replacement for traditional search engines. Yet language models are prone to making convincing but factually inaccurate claims, often referred to as 'hallucinations' In this work, we fine-tune language models to be more factual, without human labeling.
  arXiv  Detail & Related papers  (2023-11-14T18:59:15Z)
• FELM: Benchmarking Factuality Evaluation of Large Language Models [40.78878196872095]
  We introduce a benchmark for Factuality Evaluation of large Language Models, referred to as felm. We collect responses generated from large language models and annotate factuality labels in a fine-grained manner. Our findings reveal that while retrieval aids factuality evaluation, current LLMs are far from satisfactory to faithfully detect factual errors.
  arXiv  Detail & Related papers  (2023-10-01T17:37:31Z)
• Towards Reliable and Fluent Large Language Models: Incorporating Feedback Learning Loops in QA Systems [10.58737969057445]
  We build a dataset to train a critic model capable of evaluating the citation, correctness, and fluency of responses generated by large language models. We propose an automated feedback mechanism that leverages the critic model to offer real-time feedback on heterogeneous aspects of generated text. Experimental results demonstrate the efficacy of our approach, including a 4% precision increase in citation and an approximately 8% enhancement in the MAUVE metric for fluency.
  arXiv  Detail & Related papers  (2023-09-08T09:39:53Z)
• Generating Benchmarks for Factuality Evaluation of Language Models [61.69950787311278]
  We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score and perplexity do not always agree on model ranking; (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation.
  arXiv  Detail & Related papers  (2023-07-13T17:14:38Z)
• Style Over Substance: Evaluation Biases for Large Language Models [17.13064447978519]
  This study investigates the behavior of crowd-sourced and expert annotators, as well as large language models (LLMs) Our findings reveal a concerning bias in the evaluation process, as answers with factual errors are rated more favorably than answers that are too short or contained grammatical errors. We propose independently evaluating machine-generated text across multiple dimensions, rather than merging all the evaluation aspects into a single score.
  arXiv  Detail & Related papers  (2023-07-06T14:42:01Z)
• FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation [176.56131810249602]
  evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial. We introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source.
  arXiv  Detail & Related papers  (2023-05-23T17:06:00Z)

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.