Large Language Models Are Cross-Lingual Knowledge-Free Reasoners
Peng Hu, Sizhe Liu, Changjiang Gao, Xin Huang, Xue Han, Junlan Feng,, Chao Deng, and Shujian Huang

TL;DR
This paper investigates how large language models transfer reasoning abilities across languages, finding that knowledge-free reasoning transfers well due to shared neural representations, while knowledge retrieval transfer is limited by language-specific knowledge storage.
Contribution
The study decomposes reasoning into knowledge retrieval and knowledge-free reasoning, revealing that the latter transfers effectively across languages due to shared neural mechanisms.
Findings
Knowledge-free reasoning transfers nearly perfectly across languages.
Cross-lingual knowledge retrieval transfer is significantly hindered.
Shared neurons in models explain better transferability of reasoning.
Abstract
Large Language Models have demonstrated impressive reasoning capabilities across multiple languages. However, the relationship between capabilities in different languages is less explored. In this work, we decompose the process of reasoning tasks into two separated components: knowledge retrieval and knowledge-free reasoning, and analyze the relationship between cross-lingual transferability and these two components. With adapted commonsense reasoning datasets and constructed knowledge-free reasoning datasets, we show that the knowledge-free reasoning capability can be nearly perfectly transferred across various source-target language directions despite the secondary impact of resource in some specific target languages, while cross-lingual knowledge retrieval significantly hinders the transfer. Moreover, by analyzing the hidden states and feed-forward network neuron activation during…
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Taxonomy
TopicsNatural Language Processing Techniques · Topic Modeling
