FactAlign: Long-form Factuality Alignment of Large Language Models
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
66fea748343c151be4f15b22
Chao-Wei Huang
admin_assigned
2024-10-09T13:32:28.269Z
5e060e7dcbfd036a99df0dbf
https://cdn-avatars.huggingface.co/v1/production/uploads/5e060e7dcbfd036a99df0dbf/qJ31KnXhyP701B2vcot8Q.jpeg
Chao-Wei Huang
user
chaoweihuang
66fea748343c151be4f15b23
Yun-Nung Chen
FactAlign: Long-form Factuality Alignment of Large Language Models
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
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.
FactAlign fKTO Kahneman-Tversky Optimization Automatic factuality evaluation open-domain prompts information-seeking questions Factual F1 score