title: FactAlign: Long-form Factuality Alignment of Large Language Models
summary: Large language models have demonstrated significant potential as the
  next-generation information access engines. However, their reliability is
  hindered by issues of hallucination and generating non-factual content. This is
  particularly problematic in long-form responses, where assessing and ensuring
  factual accuracy is complex. In this paper, we address this gap by proposing
  FactAlign, a novel alignment framework designed to enhance the factuality of
  LLMs' long-form responses while maintaining their helpfulness. We introduce
  fKTO, a fine-grained, sentence-level alignment algorithm that extends the
  Kahneman-Tversky Optimization (KTO) alignment method. Leveraging recent
  advances in automatic factuality evaluation, FactAlign utilizes fine-grained
  factuality assessments to guide the alignment process. Our experiments on
  open-domain prompts and information-seeking questions demonstrate that
  FactAlign significantly improves the factual accuracy of LLM responses while
  also improving their helpfulness. Further analyses identify that FactAlign is
  capable of training LLMs to provide more information without losing factual
  precision, thus improving the factual F1 score. Our source code, datasets, and
  trained models are publicly available at https://github.com/MiuLab/FactAlign
paper_authors._id: 66fea748343c151be4f15b22
paper_authors.name: Chao-Wei Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-09T13:32:28.269Z
paper_authors._id: 5e060e7dcbfd036a99df0dbf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/5e060e7dcbfd036a99df0dbf/qJ31KnXhyP701B2vcot8Q.jpeg
paper_authors.fullname: Chao-Wei Huang
paper_authors.type: user
paper_authors.user: chaoweihuang
paper_authors._id: 66fea748343c151be4f15b23
paper_authors.name: Yun-Nung Chen
paper_title: FactAlign: Long-form Factuality Alignment of Large Language Models
paper_summary: Large language models have demonstrated significant potential as the
  next-generation information access engines. However, their reliability is
  hindered by issues of hallucination and generating non-factual content. This is
  particularly problematic in long-form responses, where assessing and ensuring
  factual accuracy is complex. In this paper, we address this gap by proposing
  FactAlign, a novel alignment framework designed to enhance the factuality of
  LLMs' long-form responses while maintaining their helpfulness. We introduce
  fKTO, a fine-grained, sentence-level alignment algorithm that extends the
  Kahneman-Tversky Optimization (KTO) alignment method. Leveraging recent
  advances in automatic factuality evaluation, FactAlign utilizes fine-grained
  factuality assessments to guide the alignment process. Our experiments on
  open-domain prompts and information-seeking questions demonstrate that
  FactAlign significantly improves the factual accuracy of LLM responses while
  also improving their helpfulness. Further analyses identify that FactAlign is
  capable of training LLMs to provide more information without losing factual
  precision, thus improving the factual F1 score. Our source code, datasets, and
  trained models are publicly available at https://github.com/MiuLab/FactAlign
paper_ai_summary: FactAlign enhances the factuality of large language models' long-form responses through a fine-grained sentence-level alignment algorithm, improving factual accuracy without sacrificing helpfulness.
paper_ai_keywords: FactAlign fKTO Kahneman-Tversky Optimization Automatic factuality evaluation open-domain prompts information-seeking questions Factual F1 score