Language-Specific Latent Process Hinders Cross-Lingual Performance

• URL: http://arxiv.org/abs/2505.13141v2
• Date: Wed, 28 May 2025 02:16:18 GMT
• Title: Language-Specific Latent Process Hinders Cross-Lingual Performance
• Authors: Zheng Wei Lim, Alham Fikri Aji, Trevor Cohn,
• Abstract summary: Large language models (LLMs) are capable of cross-lingual transfer, but can produce inconsistent output when prompted with the same queries written in different languages.<n>We apply the logit lens to interpret the implicit steps taken by LLMs to solve multilingual multi-choice reasoning questions.<n>We find LLMs predict inconsistently and are less accurate because they rely on subspaces of individual languages, rather than working in a shared semantic space.
• Score: 41.63308734381488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) are demonstrably capable of cross-lingual transfer, but can produce inconsistent output when prompted with the same queries written in different languages. To understand how language models are able to generalize knowledge from one language to the others, we apply the logit lens to interpret the implicit steps taken by LLMs to solve multilingual multi-choice reasoning questions. We find LLMs predict inconsistently and are less accurate because they rely on subspaces of individual languages, rather than working in a shared semantic space. While larger models are more multilingual, we show their hidden states are more likely to dissociate from the shared representation compared to smaller models, but are nevertheless more capable of retrieving knowledge embedded across different languages. Finally, we demonstrate that knowledge sharing can be modulated by steering the models' latent processing towards the shared semantic space. We find reinforcing utilization of the shared space improves the models' multilingual reasoning performance, as a result of more knowledge transfer from, and better output consistency with English.

Related papers

• When Less Language is More: Language-Reasoning Disentanglement Makes LLMs Better Multilingual Reasoners [111.50503126693444]
  We show that language-specific ablation consistently boosts multilingual reasoning performance.<n>Compared to post-training, our training-free ablation achieves comparable or superior results with minimal computational overhead.
  arXiv  Detail & Related papers  (2025-05-21T08:35:05Z)
• High-Dimensional Interlingual Representations of Large Language Models [65.77317753001954]
  Large language models (LLMs) trained on massive multilingual datasets hint at the formation of interlingual constructs.<n>We explore 31 diverse languages varying on their resource-levels, typologies, and geographical regions.<n>We find that multilingual LLMs exhibit inconsistent cross-lingual alignments.
  arXiv  Detail & Related papers  (2025-03-14T10:39:27Z)
• Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages [15.203789021094982]
  In large language models (LLMs), how are multiple languages learned and encoded?<n>We train sparse autoencoders on Llama-3-8B and Aya-23-8B, and demonstrate that abstract grammatical concepts are often encoded in feature directions shared across many languages.
  arXiv  Detail & Related papers  (2025-01-10T21:18:21Z)
• Thank You, Stingray: Multilingual Large Language Models Can Not (Yet) Disambiguate Cross-Lingual Word Sense [30.62699081329474]
  We introduce a novel benchmark for cross-lingual sense disambiguation, StingrayBench. We collect false friends in four language pairs, namely Indonesian-Malay, Indonesian-Tagalog, Chinese-Japanese, and English-German. In our analysis of various models, we observe they tend to be biased toward higher-resource languages.
  arXiv  Detail & Related papers  (2024-10-28T22:09:43Z)
• Beneath the Surface of Consistency: Exploring Cross-lingual Knowledge Representation Sharing in LLMs [31.893686987768742]
  Language models are inconsistent in their ability to answer the same factual question across languages. We explore multilingual factual knowledge through two aspects: the model's ability to answer a query consistently across languages, and the ability to ''store'' answers in a shared representation for several languages.
  arXiv  Detail & Related papers  (2024-08-20T08:38:30Z)
• Understanding and Mitigating Language Confusion in LLMs [76.96033035093204]
  We evaluate 15 typologically diverse languages with existing and newly-created English and multilingual prompts.<n>We find that Llama Instruct and Mistral models exhibit high degrees of language confusion.<n>We find that language confusion can be partially mitigated via few-shot prompting, multilingual SFT and preference tuning.
  arXiv  Detail & Related papers  (2024-06-28T17:03:51Z)
• 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)
• Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners [67.85635044939836]
  Large Language Models (LLMs) have shown impressive language capabilities. In this work, we investigate the spontaneous multilingual alignment improvement of LLMs. We find that LLMs instruction-tuned on the question translation data (i.e. without annotated answers) are able to encourage the alignment between English and a wide range of languages.
  arXiv  Detail & Related papers  (2024-05-22T16:46:19Z)

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.