Paper: LLMs face fundamental limits as general-purpose solvers via prompting

A new paper argues that large language models (LLMs) are not truly general-purpose solvers due to fundamental constraints of prompt-based communication. The research suggests that language itself is a limited channel for conveying task information, and alignment constraints can further distort task interpretation. These limitations create an irreducible error floor, meaning that even with infinite data or increased model scale, certain tasks may remain unsolvable through prompting alone. AI

IMPACT Suggests that current prompting methods have inherent limitations for LLMs, potentially necessitating new interfaces like multimodal inputs or external memory for broader problem-solving capabilities.

RANK_REASON The cluster contains an academic paper discussing theoretical limitations of LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

• large-language models
• PAC-bayesian learning

• paper
• model release

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