Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through
  the Lens of Internal Representations
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.
6806548d7593cbf4c6c2ca2e
Chenghao Xiao
claimed_verified
2025-04-22T09:52:06.160Z
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chenghao xiao
user
gowitheflow
6806548d7593cbf4c6c2ca2f
Hou Pong Chan
claimed_verified
2025-06-10T15:20:47.592Z
604f67ef0fe8ff3ec13d71ef
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Hou Pong (Ken) Chan
user
kenchan0226
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Hao Zhang
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2025-06-10T11:21:06.498Z
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ZHANG HAO
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26hzhang
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Mahani Aljunied
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Lidong Bing
claimed_verified
2025-07-22T07:54:48.250Z
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Lidong Bing
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LidongBing
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Noura Al Moubayed
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Yu Rong
claimed_verified
2025-06-10T09:30:13.319Z
642eecbf9b2484d7d8526781
https://cdn-avatars.huggingface.co/v1/production/uploads/642eecbf9b2484d7d8526781/4IvGbd66s49Wx5pZyZGHA.png
Yu Rong
user
Swrooy
Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through
  the Lens of Internal Representations
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.
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.
internal representations knowledge boundaries multilingual evaluation suite training-free alignment method bilingual fine-tuning