title: Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through
    the Lens of Internal Representations
summary: While understanding the knowledge boundaries of LLMs is crucial to prevent
  hallucination, research on knowledge boundaries of LLMs has predominantly
  focused on English. In this work, we present the first study to analyze how
  LLMs recognize knowledge boundaries across different languages by probing their
  internal representations when processing known and unknown questions in
  multiple languages. Our empirical studies reveal three key findings: 1) LLMs'
  perceptions of knowledge boundaries are encoded in the middle to middle-upper
  layers across different languages. 2) Language differences in knowledge
  boundary perception follow a linear structure, which motivates our proposal of
  a training-free alignment method that effectively transfers knowledge boundary
  perception ability across languages, thereby helping reduce hallucination risk
  in low-resource languages; 3) Fine-tuning on bilingual question pair
  translation further enhances LLMs' recognition of knowledge boundaries across
  languages. Given the absence of standard testbeds for cross-lingual knowledge
  boundary analysis, we construct a multilingual evaluation suite comprising
  three representative types of knowledge boundary data. Our code and datasets
  are publicly available at
  https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries.
paper_authors._id: 6806548d7593cbf4c6c2ca2e
paper_authors.name: Chenghao Xiao
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:52:06.160Z
paper_authors._id: 63108cc834c7d77420b0fd68
paper_authors.avatarUrl: /avatars/2721e573a417a8ec0b81ee048c4b42ba.svg
paper_authors.fullname: chenghao xiao
paper_authors.type: user
paper_authors.user: gowitheflow
paper_authors._id: 6806548d7593cbf4c6c2ca2f
paper_authors.name: Hou Pong Chan
paper_authors._id: 6806548d7593cbf4c6c2ca30
paper_authors.name: Hao Zhang
paper_authors._id: 6806548d7593cbf4c6c2ca31
paper_authors.name: Mahani Aljunied
paper_authors._id: 6806548d7593cbf4c6c2ca32
paper_authors.name: Lidong Bing
paper_authors._id: 6806548d7593cbf4c6c2ca33
paper_authors.name: Noura Al Moubayed
paper_authors._id: 6806548d7593cbf4c6c2ca34
paper_authors.name: Yu Rong
paper_title: Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through
    the Lens of Internal Representations
paper_summary: While understanding the knowledge boundaries of LLMs is crucial to prevent
  hallucination, research on knowledge boundaries of LLMs has predominantly
  focused on English. In this work, we present the first study to analyze how
  LLMs recognize knowledge boundaries across different languages by probing their
  internal representations when processing known and unknown questions in
  multiple languages. Our empirical studies reveal three key findings: 1) LLMs'
  perceptions of knowledge boundaries are encoded in the middle to middle-upper
  layers across different languages. 2) Language differences in knowledge
  boundary perception follow a linear structure, which motivates our proposal of
  a training-free alignment method that effectively transfers knowledge boundary
  perception ability across languages, thereby helping reduce hallucination risk
  in low-resource languages; 3) Fine-tuning on bilingual question pair
  translation further enhances LLMs' recognition of knowledge boundaries across
  languages. Given the absence of standard testbeds for cross-lingual knowledge
  boundary analysis, we construct a multilingual evaluation suite comprising
  three representative types of knowledge boundary data. Our code and datasets
  are publicly available at
  https://github.com/DAMO-NLP-SG/LLM-Multilingual-Knowledge-Boundaries.
paper_ai_summary: LLMs' knowledge boundary perception is encoded in specific layers and exhibits linear differences across languages; a training-free alignment method and bilingual fine-tuning improve cross-lingual knowledge boundary recognition.
paper_ai_keywords: internal representations knowledge boundaries multilingual evaluation suite training-free alignment method bilingual fine-tuning