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Natural language training yields superior brain-aligned geometry in LLMs over code

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv investigates whether training language models on code can replicate the brain-aligned geometry achieved through natural language training. Researchers trained GPT-2 small models on various datasets including TinyStories (natural language), Python, C++, and assembly code. Results indicate that TinyStories training significantly outperformed code training in achieving brain-aligned representations on specific benchmarks like Pereira-243, suggesting that natural language is crucial for developing distinct representational geometries that closely correspond to neural data. AI

IMPACT This research suggests that natural language training is essential for developing language models with representations that better align with human brain activity, potentially guiding future model development.

RANK_REASON The cluster contains a research paper detailing experimental results on language model training. [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 →

Natural language training yields superior brain-aligned geometry in LLMs over code

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The cluster contains a research paper detailing experimental results on language model training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · I\~nigo Parra ·

    Language, Not Code Abstraction: Natural-language Training Shapes Brain-aligned Geometry

    arXiv:2611.09027v1 Announce Type: new Abstract: Code-trained language models produce positive brain scores, which raises the question of whether abstract structure learned from programming languages can substitute for natural-language training. We trained 25 GPT-2-small models on…

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