aiqbench analysis
This research isolates specific attention heads called Retrieval-Transition heads (RTHs) in multilingual large language models, demonstrating their critical role in governing cross-lingual output generation and Chain-of-Thought reasoning. The study reveals that these heads are distinct from standard retrieval mechanisms and are more essential for model performance than previously understood, particularly when masking them causes significant degradation across major benchmarks like Qwen-2.5 and Llama-3.1.
Bridging Latent Reasoning and Target-Language Generation via Retrieval-Transition Heads
arXiv:2602.22453v3 Announce Type: replace Abstract: Recent work has identified a subset of attention heads in Transformer as retrieval heads, which are responsible for retrieving information from the context. In this work, we first investigate retrieval heads in multilingual contexts. In multilingual language models, we find that retrieval heads are often shared across multiple languages. Expanding the study to cross-lingual setting, we identify Retrieval-Transition heads(RTH), which govern the transition to specific target-language output. Our experiments reveal that RTHs are distinct from retrieval heads and more vital for Chain-of-Thought reasoning in multilingual LLMs. Across four multilingual benchmarks (MMLU-ProX, MGSM, MLQA, and XQuaD) and two model families (Qwen-2.5 and Llama-3.1), we demonstrate that masking RTH induces bigger performance drop than masking Retrieval Heads (RH). Our work advances understanding of multilingual LMs by isolating the attention heads responsible for mapping to target languages.
Abstract:Recent work has identified a subset of attention heads in Transformer as retrieval heads, which are responsible for retrieving information from the context. In this work, we first investigate retrieval heads in multilingual contexts. In multilingual language models, we find that retrieval heads are often shared across multiple languages. Expanding the study to cross-lingual setting, we identify Retrieval-Transition heads(RTH), which govern the transition to specific target-language output. Our experiments reveal that RTHs are distinct from retrieval heads and more vital for Chain-of-Thought reasoning in multilingual LLMs. Across four multilingual benchmarks (MMLU-ProX, MGSM, MLQA, and XQuaD) and two model families (Qwen-2.5 and Llama-3.1), we demonstrate that masking RTH induces bigger performance drop than masking Retrieval Heads (RH). Our work advances understanding of multilingual LMs by isolating the attention heads responsible for mapping to target languages.
Computation and Language (cs.CL)
arXiv:2602.22453 [cs.CL]
(or arXiv:2602.22453v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.22453 Focus to learn more arXiv-issued DOI via DataCite
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