Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs
Large language models (LLMs) exhibit hallucinations (i.e., unfaithful or
nonsensical information) when serving as AI assistants in various domains.
Since hallucinations always come with truthful content in the LLM responses,
previous factuality alignment methods that conduct response-level preference
learning inevitably introduced noises during training. Therefore, this paper
proposes a fine-grained factuality alignment method based on Direct Preference
Optimization (DPO), called Mask-DPO. Incorporating sentence-level factuality as
mask signals, Mask-DPO only learns from factually correct sentences in the
preferred samples and prevents the penalty on factual contents in the not
preferred samples, which resolves the ambiguity in the preference learning.
Extensive experimental results demonstrate that Mask-DPO can significantly
improve the factuality of LLMs responses to questions from both in-domain and
out-of-domain datasets, although these questions and their corresponding topics
are unseen during training. Only trained on the ANAH train set, the score of
Llama3.1-8B-Instruct on the ANAH test set is improved from 49.19% to 77.53%,
even surpassing the score of Llama3.1-70B-Instruct (53.44%), while its
FactScore on the out-of-domain Biography dataset is also improved from 30.29%
to 39.39%. We further study the generalization property of Mask-DPO using
different training sample scaling strategies and find that scaling the number
of topics in the dataset is more effective than the number of questions. We
provide a hypothesis of what factual alignment is doing with LLMs, on the
implication of this phenomenon, and conduct proof-of-concept experiments to
verify it. We hope the method and the findings pave the way for future research
on scaling factuality alignment.
67c7c3ce6f68759bf368533c
Yuzhe Gu
claimed_verified
2025-03-05T10:11:38.988Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6601196cc91ba4c08ad6e270/X2YPNzUOQXBz5Gv-xR9LW.jpeg
yuzhe gu
user
vanilla1116
67c7c3ce6f68759bf368533d
Wenwei Zhang
admin_assigned
2025-03-05T10:32:26.106Z
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/avatars/18958b8406d1ce492b54c1c839f18c54.svg
Wenwei Zhang
user
ZwwWayne
67c7c3ce6f68759bf368533e
Chengqi Lyu
67c7c3ce6f68759bf368533f
Dahua Lin
admin_assigned
2025-03-05T10:32:37.093Z
636317ed80c1a705a6eff396
/avatars/3db090e101b916d9256d0d3e043db71d.svg
Dahua Lin
user
lindahua
67c7c3ce6f68759bf3685340
Kai Chen
Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs
Large language models (LLMs) exhibit hallucinations (i.e., unfaithful or
nonsensical information) when serving as AI assistants in various domains.
Since hallucinations always come with truthful content in the LLM responses,
previous factuality alignment methods that conduct response-level preference
learning inevitably introduced noises during training. Therefore, this paper
proposes a fine-grained factuality alignment method based on Direct Preference
Optimization (DPO), called Mask-DPO. Incorporating sentence-level factuality as
mask signals, Mask-DPO only learns from factually correct sentences in the
preferred samples and prevents the penalty on factual contents in the not
preferred samples, which resolves the ambiguity in the preference learning.
Extensive experimental results demonstrate that Mask-DPO can significantly
improve the factuality of LLMs responses to questions from both in-domain and
out-of-domain datasets, although these questions and their corresponding topics
are unseen during training. Only trained on the ANAH train set, the score of
Llama3.1-8B-Instruct on the ANAH test set is improved from 49.19% to 77.53%,
even surpassing the score of Llama3.1-70B-Instruct (53.44%), while its
FactScore on the out-of-domain Biography dataset is also improved from 30.29%
to 39.39%. We further study the generalization property of Mask-DPO using
different training sample scaling strategies and find that scaling the number
of topics in the dataset is more effective than the number of questions. We
provide a hypothesis of what factual alignment is doing with LLMs, on the
implication of this phenomenon, and conduct proof-of-concept experiments to
verify it. We hope the method and the findings pave the way for future research
on scaling factuality alignment.
Mask-DPO, a fine-grained factuality alignment method based on Direct Preference Optimization, improves the factuality of LLM responses by learning from factually correct sentences and generalizes to unseen questions and topics.
Large language models (LLMs) hallucinations Direct Preference Optimization (DPO) sentence-level factuality Mask-DPO factuality alignment