Paper finds LLM skills vary by language; English reasoning recovers performance
LChoshen · x · 2026-08-25
Retweeted a paper titled "Skill Issue: Are Skills Language-Invariant in LLMs?" The research reveals significant gaps in reasoning and strategy when multilingual LLMs operate in different languages, even with fixed game rules. Surprisingly, switching the reasoning language to English often recovers most of the lost performance, suggesting that while skills may exist within the model, the language interface dictates accessibility.
More from Models
- Alibaba Teases Qwen4 Architecture, Announces Open Source Qwen3.8-Flash-Next — bclavie · 2026-08-25
- Diffusion Models Scale Like LLMs, But Need 10x Data Per Parameter — burny_tech · 2026-08-25
- ChatGPT caught searching specific subreddits by name despite claims — gaganghotra_ · 2026-08-25
- LLM Memory Often Makes Things Worse — Maybe Forgetting Is the Optimal Process — sebpaquet · 2026-08-25
- Zhipu GLM 5.3 Flash interface potentially leaked online — LegacyRemaster · 2026-08-25
- Dynamic quants of Qwen3.8 27B fix 'caveman thinking' issue — Glad_Claim_6287 · 2026-08-25