New paper questions LLM general-purpose learning limits due to language constraints

A new arXiv paper argues that large language models (LLMs) are not truly general-purpose learners due to fundamental constraints imposed by natural language as an interface. The research introduces the concepts of an "expressivity floor" and an "objective-misalignment floor," suggesting that language's limited capacity and alignment restrictions create irreducible error floors. These limitations mean that even with infinite data, prompt-conditioned LLMs may be unable to solve certain task families correctly, indicating a need for interfaces beyond natural language. AI

IMPACT Suggests inherent limitations in prompt-based LLMs, potentially driving research into alternative interfaces.

RANK_REASON Academic paper published on arXiv discussing theoretical limits of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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• Hugging Face
• large-language models
• PAC-bayesian learning

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