Wen Luo Peking University, Guangyue Peng Peking University, Wei Li, Shaohang Wei Peking University, Feifan Song Peking University, Liang Wang Microsoft Research, Nan Yang Microsoft Research Asia, Xingxing Zhang Microsoft Research Asia 等 11 位作者
Despite their impressive capabilities, large language models (LLMs) frequently generate hallucinations. Previous work shows that their internal states encode rich signals of truthfulness, yet the origins and mechanisms of these signals remain unclear. In this paper, we demonstrate that truthfulness cues arise from two distinct information pathways: (1) a Question-Anchored pathway that depends on question–answer information flow, and (2) an Answer-Anchored pathway that derives self-contained evidence from the generated answer itself. First, we validate and disentangle these pathways through attention knockout and token patching. Afterwards, we uncover notable and intriguing properties of these two mechanisms. Further experiments reveal that (1) the two mechanisms are closely associated with LLM knowledge boundaries; and (2) internal representations are aware of their distinctions. Finally, building on these insightful findings, two applications are proposed to enhance hallucination detection performance. Overall, our work provides new insight into how LLMs internally encode truthfulness, offering directions for more reliable and self-aware generative systems.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jing Jin, Yuhan Song Peking University, Wen Luo Peking University, Houfeng Wang
The generation of Retrieval-Augmented Generation (RAG) models is affected by factors such as the quality and order of input documents, indicating that their ability to utilize documents remains underdeveloped. This ability encompasses not only identifying useful documents from inputs but also minimizing positional bias and filtering irrelevant documents. To achieve this, key challenges include the model’s internal estimation of document importance and positional bias. In this paper, we conduct a comprehensive study on the properties of attention weights, examining the impact of factors like aggregation methods, document quality, document position, token type, and so on. Based on our findings, we propose strategies to enhance document utilization from three perspectives: document ranking, placement, and filtering. Comprehensive experiments show that our method outperforms baselines and improves document utilization effectiveness in a training-free manner.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Ziyao Xu Peking University, Cong Wang Guangdong OPPO Mobile Telecommunications Corp.,Ltd., Houfeng Wang
Compositional generalization tests are often used to estimate the compositionality of LLMs. However, such tests have the following limitations: (1) they only focus on the output results without considering LLMs’ understanding of sample compositionality, resulting in explainability defects; (2) they rely on dataset partition to form the test set with combinations unseen in the training set, suffering from combination leakage issues. In this work, we propose a novel rule-generation perspective for compositionality estimation for LLMs. It requires LLMs to generate a program as rules for dataset mapping and provides estimates of the compositionality of LLMs using complexity-based theory. The perspective addresses the limitations of compositional generalization tests and provides a new way to analyze the compositionality characterization of LLMs. We conduct experiments and analysis of existing advanced LLMs based on this perspective on a string-to-grid task, and find various compositionality characterizations and compositionality deficiencies exhibited by LLMs.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表