用细粒度批评反馈提升大模型事实性，效果优于当前最佳方法。
Improving Model Factuality with Fine-grained Critique-based Evaluator
- 训练评估器FenCE，对生成内容逐条给出评分和文本批评。
- 在FActScore上使Llama2和Llama3事实性分别提升16.86%和14.45%。
- 适合关注大模型事实性优化的研究者与开发者使用。
事实性评估旨在检测语言模型生成的事实错误，从而指导更准确模型的开发。为此，我们训练了一个名为FenCE的事实性评估器，为语言模型生成器提供声明级别的事实性反馈。通过结合多个公开判断数据集进行数据增强，FenCE能够（1）生成带评分的文本批评，（2）基于多种工具获取的多样化源文档做出声明级判断。随后，我们提出一个利用FenCE改进语言模型事实性的框架：生成候选回答，借助FenCE在不引入新知识的前提下修正并评分每条回答，再通过偏好高分修正后回答来训练生成器。实验表明，我们的数据增强方法使评估器在LLM-AggreFact上的准确率提升2.9%。使用FenCE后，Llama2-7B-chat和Llama3-8B-chat在FActScore上的事实性分别提升16.86%和14.45%，超越当前最优微调方法8.83%和6.96%。
原文摘要 · Abstract (English)
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%.
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