Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations

• URL: http://arxiv.org/abs/2504.13816v1
• Date: Fri, 18 Apr 2025 17:44:12 GMT
• Title: Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations
• Authors: Chenghao Xiao, Hou Pong Chan, Hao Zhang, Mahani Aljunied, Lidong Bing, Noura Al Moubayed, Yu Rong,
• Abstract summary: We present the first study to analyze how LLMs recognize knowledge boundaries across different languages.<n>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.
• Score: 72.62400923539234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set [28.592959007943538]
  This work investigates whether large language models (LLMs) capture discourse knowledge that generalizes across languages and frameworks. Using multilingual discourse relation classification as a testbed, we examine a comprehensive set of 23 LLMs of varying sizes and multilingual capabilities. Our results show that LLMs, especially those with multilingual training corpora, can generalize discourse information across languages and frameworks.
  arXiv  Detail & Related papers  (2025-03-13T16:20:25Z)
• How does a Multilingual LM Handle Multiple Languages? [0.0]
  This study critically examines capabilities in multilingual understanding, semantic representation, and cross-lingual knowledge transfer. It assesses semantic similarity by analyzing multilingual word embeddings for consistency using cosine similarity. It examines BLOOM-1.7B and Qwen2 through Named Entity Recognition and sentence similarity tasks to understand their linguistic structures.
  arXiv  Detail & Related papers  (2025-02-06T18:08:14Z)
• Knowledge Boundary of Large Language Models: A Survey [75.67848187449418]
  Large language models (LLMs) store vast amount of knowledge in their parameters, but they still have limitations in the memorization and utilization of certain knowledge.<n>This highlights the critical need to understand the knowledge boundary of LLMs, a concept that remains inadequately defined in existing research.<n>We propose a comprehensive definition of the LLM knowledge boundary and introduce a formalized taxonomy categorizing knowledge into four distinct types.
  arXiv  Detail & Related papers  (2024-12-17T02:14:02Z)
• One Mind, Many Tongues: A Deep Dive into Language-Agnostic Knowledge Neurons in Large Language Models [19.58983929459173]
  Large language models (LLMs) have learned vast amounts of factual knowledge through self-supervised pre-training on large-scale corpora. LLMs have also demonstrated excellent multilingual capabilities, which can express the learned knowledge in multiple languages.
  arXiv  Detail & Related papers  (2024-11-26T13:03:49Z)
• How Do Multilingual Language Models Remember Facts? [50.13632788453612]
  We show that previously identified recall mechanisms in English largely apply to multilingual contexts.<n>We localize the role of language during recall, finding that subject enrichment is language-independent.<n>In decoder-only LLMs, FVs compose these two pieces of information in two separate stages.
  arXiv  Detail & Related papers  (2024-10-18T11:39:34Z)
• Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models [62.91524967852552]
  Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora.<n>But can these models relate corresponding concepts across languages, i.e., be crosslingual?<n>This study evaluates state-of-the-art LLMs on inherently crosslingual tasks.
  arXiv  Detail & Related papers  (2024-06-23T15:15:17Z)
• Teaching LLMs to Abstain across Languages via Multilingual Feedback [40.84205285309612]
  We show that multilingual feedback helps identify knowledge gaps across diverse languages, cultures, and communities.<n>Extensive experiments demonstrate that our multilingual feedback approach outperforms various strong baselines.<n>Further analysis reveals that multilingual feedback is both an effective and a more equitable abstain strategy to serve diverse language speakers.
  arXiv  Detail & Related papers  (2024-06-22T21:59:12Z)
• FAC$^2$E: Better Understanding Large Language Model Capabilities by Dissociating Language and Cognition [56.76951887823882]
  Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. We present FAC$2$E, a framework for Fine-grAined and Cognition-grounded LLMs' Capability Evaluation.
  arXiv  Detail & Related papers  (2024-02-29T21:05:37Z)
• Exploring Multilingual Concepts of Human Value in Large Language Models: Is Value Alignment Consistent, Transferable and Controllable across Languages? [34.38469832305664]
  This paper focuses on human values-related concepts (i.e., value concepts) due to their significance for AI safety. We first empirically confirm the presence of value concepts within LLMs in a multilingual format. Further analysis on the cross-lingual characteristics of these concepts reveals 3 traits arising from language resource disparities.
  arXiv  Detail & Related papers  (2024-02-28T07:18:39Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.