{"id":1184638,"date":"2024-12-18T00:00:00","date_gmt":"2024-12-18T08:00:00","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=1184638"},"modified":"2026-08-24T07:33:52","modified_gmt":"2026-08-24T14:33:52","slug":"context-dpo-aligning-language-models-for-context-faithfulness","status":"publish","type":"msr-research-item","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/publication\/context-dpo-aligning-language-models-for-context-faithfulness\/","title":{"rendered":"Context-DPO: Aligning Language Models for Context-Faithfulness"},"content":{"rendered":"\n\n\n<p class=\"wp-block-paragraph\">Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intentions and values, improving context-faithfulness through alignment remains underexplored. To address this, we propose <math><mtext>Context-DPO<\/mtext><\/math>, the first alignment method specifically designed to enhance LLMs&#8217; context-faithfulness. We introduce <math><mtext>ConFiQA<\/mtext><\/math>, a benchmark that simulates Retrieval-Augmented Generation (RAG) scenarios with knowledge conflicts to evaluate context-faithfulness. By leveraging faithful and stubborn responses to questions with provided context from ConFiQA, our Context-DPO aligns LLMs through direct preference optimization. Extensive experiments demonstrate that our Context-DPO significantly improves context-faithfulness, achieving 35% to 280% improvements on popular open-source models. Further analysis demonstrates that Context-DPO preserves LLMs&#8217; generative capabilities while providing interpretable insights into context utilization. Our code and data are released at https:\/\/github.com\/byronBBL\/Context-DPO<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Reliable responses from large language models (LLMs) require adherence to user instructions and retrieved information. While alignment techniques help LLMs align with human intentions and values, improving context-faithfulness through alignment remains underexplored. To address this, we propose Context-DPO, the first alignment method specifically designed to enhance LLMs&#8217; context-faithfulness. We introduce ConFiQA, a benchmark that simulates [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Baolong Bi","user_id":0},{"type":"user_nicename","value":"Shaohan Huang","user_id":"39709"},{"type":"text","value":"Yiwei Wang","user_id":0},{"type":"user_nicename","value":"Tianchi Yang","user_id":"44287"},{"type":"text","value":"Zihan Zhang","user_id":0},{"type":"user_nicename","value":"Haizhen Huang","user_id":"32007"},{"type":"text","value":"Lingrui Mei","user_id":0},{"type":"text","value":"Junfeng Fang","user_id":0},{"type":"text","value":"Zehao Li","user_id":0},{"type":"user_nicename","value":"Furu Wei","user_id":"31830"},{"type":"text","value":"Weiwei Deng","user_id":0},{"type":"text","value":"Feng Sun","user_id":0},{"type":"text","value":"Qi 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