Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization

• URL: http://arxiv.org/abs/2501.13573v1
• Date: Thu, 23 Jan 2025 11:23:25 GMT
• Title: Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization
• Authors: Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yangfan Ye, Weihong Zhong, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Bing Qin,
• Abstract summary: We propose RHIO, a framework to teach large language models to explicitly discriminate between faithful and unfaithful generations.<n> RHIO first augments unfaithful samples that simulate realistic model-intrinsic errors by selectively masking retrieval heads.<n>These samples are incorporated into joint training, enabling the model to distinguish unfaithful outputs from faithful ones conditioned on control tokens.
• Score: 35.269343563526675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensuring contextual faithfulness in retrieval-augmented large language models (LLMs) is crucial for building trustworthy information-seeking systems, particularly in long-form question-answering (LFQA) scenarios. In this work, we identify a salient correlation between LFQA faithfulness and retrieval heads, a set of attention heads responsible for retrieving contextual information. Leveraging this insight, we propose RHIO, a framework designed to teach LLMs to explicitly discriminate between faithful and unfaithful generations. RHIO first augments unfaithful samples that simulate realistic model-intrinsic errors by selectively masking retrieval heads. Then, these samples are incorporated into joint training, enabling the model to distinguish unfaithful outputs from faithful ones conditioned on control tokens. Furthermore, these control tokens are leveraged to self-induce contrastive outputs, amplifying their difference through contrastive decoding. Additionally, to facilitate the evaluation of contextual faithfulness, we also introduce GroundBench, a comprehensive benchmark compiled from five existing LFQA datasets. Extensive experimental results on GroundBench demonstrate that RHIO significantly improves faithfulness, even outperforming GPT-4o.

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