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    <abstract>Large Language Models (LLMs) enhanced with external contexts, such as through retrieval-augmented generation (RAG), often face challenges in handling imperfect evidence. They tend to over-rely on external knowledge, making them vulnerable to misleading and unhelpful contexts. To address this, we propose the concept of context-robust LLMs, which can effectively balance internal knowledge with external context, similar to human cognitive processes. Specifically, context-robust LLMs should rely on external context only when lacking internal knowledge, identify contradictions between internal and external knowledge, and disregard unhelpful contexts. To achieve this goal, we introduce Grft, a lightweight and plug-and-play gated representation fine-tuning approach. Grft consists of two key components: a gating mechanism to detect and filter problematic inputs, and low-rank representation adapters to adjust hidden representations. By training a lightweight intervention function with only 0.0004% of model size on fewer than 200 examples, Grft can effectively adapt LLMs towards context-robust behaviors.</abstract>
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%0 Conference Proceedings
%T Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach
%A Zeng, Shenglai
%A He, Pengfei
%A Guo, Kai
%A Zheng, Tianqi
%A Lu, Hanqing
%A Xing, Yue
%A Liu, Hui
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F zeng-etal-2025-towards-context
%X Large Language Models (LLMs) enhanced with external contexts, such as through retrieval-augmented generation (RAG), often face challenges in handling imperfect evidence. They tend to over-rely on external knowledge, making them vulnerable to misleading and unhelpful contexts. To address this, we propose the concept of context-robust LLMs, which can effectively balance internal knowledge with external context, similar to human cognitive processes. Specifically, context-robust LLMs should rely on external context only when lacking internal knowledge, identify contradictions between internal and external knowledge, and disregard unhelpful contexts. To achieve this goal, we introduce Grft, a lightweight and plug-and-play gated representation fine-tuning approach. Grft consists of two key components: a gating mechanism to detect and filter problematic inputs, and low-rank representation adapters to adjust hidden representations. By training a lightweight intervention function with only 0.0004% of model size on fewer than 200 examples, Grft can effectively adapt LLMs towards context-robust behaviors.
%R 10.18653/v1/2025.acl-long.506
%U https://aclanthology.org/2025.acl-long.506/
%U https://doi.org/10.18653/v1/2025.acl-long.506
%P 10262-10276

Markdown (Informal)

[Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach](https://aclanthology.org/2025.acl-long.506/) (Zeng et al., ACL 2025)

• Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach (Zeng et al., ACL 2025)

ACL

• Shenglai Zeng, Pengfei He, Kai Guo, Tianqi Zheng, Hanqing Lu, Yue Xing, and Hui Liu. 2025. Towards Context-Robust LLMs: A Gated Representation Fine-tuning Approach. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 10262–10276, Vienna, Austria. Association for Computational Linguistics.