Investigating the Role of Reasoning-Language Alignment in Monolingual Retrieval-Augmented Generation
German RAG tests show reasoning-language alignment helps, but unconstrained English still matches it.
Researchers build a fully monolingual German retrieval-augmented question-answering testbed on The Dark Eye, a niche domain that requires models to use retrieved evidence rather than memorized knowledge. Forcing German reasoning outperforms forced French even though the model benchmarks higher in French, indicating the gain comes from alignment with the query and documents. The advantage grows when retrieved context is richer and structure-aware. Forced German only reaches, and does not surpass, the model's unconstrained English reasoning; the testbed and benchmark are publicly released.
- German Dark Eye testbed forces answers to rely on retrieval.
- Forced German reasoning beats forced French despite lower German proficiency.
- The advantage grows with richer structure-aware retrieved context.
- Unconstrained English reasoning still matches forced German reasoning.
From the original article
1 min read at huggingface.co
Reasoning traces improve large language models (LLMs), but current models are trained to reason mostly in English. It has been shown that forcing a model to reason in another language degrades accuracy, even when the reasoning language matches the language of the prompt -- but only for a setting where the model reasons over a short prompt.
By Oliver Hauck, Mario Sanz-Guerrero, Katharina von der Wense · huggingface.co
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