Language as a Latent Variable for Reasoning Optimization
As LLMs reduce English-centric bias, a surprising trend emerges: non-English responses sometimes outperform English on reasoning tasks. We hypothesize that language functions as a latent variable that structurally modulates the model's internal inference pathways, rather than merely serving as an output medium. To test this, we conducted a Polyglot Thinking Experiment, in which models were prompted to solve identical problems under language-constrained and language-unconstrained conditions. Results show that non-English responses often achieve higher accuracy, and the best performance frequently occur when language is unconstrained, suggesting that multilinguality broadens the model's lat...
| Authors | Linjuan Wu, Haoran Wei, Jialong Tang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 23 April 2026 |
| Last updated at source | 3 May 2026 |
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