LLMs struggle to act on encoded geometric reasoning, study finds

A new research paper explores the gap between what large language models (LLMs) encode and their ability to act on that information, specifically in the domain of geometric reasoning. The study found that while LLMs can decode local geometric relations effectively after pretraining, information about sketch-level constraints is already highly decodable from randomly initialized representations. Furthermore, the research indicates that decodable information does not always translate to actionable behavior, as generation often fails to express this information, and intervention methods do not reliably control outputs. This divergence highlights distinct failures in encoding versus expressing or controlling encoded geometric structure. AI

IMPACT Highlights limitations in LLM reasoning and action, suggesting current models may not fully leverage encoded knowledge for complex tasks.

RANK_REASON Academic paper on LLM capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• computer-aided design
• depth of field
• Hugging Face
• large language models
• LLMs

• paper
• model release

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