New research reveals LLM overconfidence stems from specific internal circuits

A new research paper titled "Wired for Overconfidence" explores the phenomenon of large language models (LLMs) confidently providing incorrect information. The study identifies specific circuits within LLMs, primarily composed of MLP blocks and attention heads in middle-to-late layers, that are responsible for generating this inflated verbalized confidence. Researchers demonstrated that by intervening in these circuits during inference, they could significantly improve the models' calibration and reduce overconfidence. AI

IMPACT Identifies specific internal mechanisms driving LLM overconfidence, potentially leading to better calibration and more reliable AI systems.

RANK_REASON The cluster contains a research paper published on arXiv detailing a mechanistic analysis of LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• attention heads
• LLMs
• MLP blocks
• Tianyi Zhao

• paper
• safety

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