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New research highlights English bias in LLMs, calls for per-language investment

By PulseAugur Editorial · [1 sources] ·

A new paper reveals that large language models are significantly biased towards English, even when fine-tuned for other languages. Researchers found that continual pre-training does not improve cultural understanding in a target language cost-effectively compared to training from scratch. This suggests that future LLM development may require dedicated investment in per-language resources rather than solely expanding English-centric ones. AI

IMPACT Suggests a shift towards dedicated per-language LLM development, potentially increasing costs and complexity for non-English applications.

RANK_REASON Academic paper analyzing LLM language bias. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research highlights English bias in LLMs, calls for per-language investment

COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ukyo Honda ·

    Toward LLMs Beyond English-Centric Development

    Through an analysis of sequences generated by open-weight large language models (LLMs), we demonstrate that LLMs are heavily biased toward English. While continual pre-training is commonly used to adapt LLMs to a target language, we show that it does not offer a cost advantage ov…

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