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LLMs use "direction of ignorance" to temper Bayesian priors

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a geometric property within Large Language Models (LLMs) that quantifies their reliance on prior knowledge when faced with limited context. This "direction of ignorance" is encoded in the unembedding matrix and acts as a Bayesian prior, which the model defaults to when uncertain. This phenomenon was observed across various model families including Llama, Qwen, Gemma, and Pythia, regardless of their parameter size. AI

IMPACT This research offers a new lens for understanding and potentially controlling LLM uncertainty and calibration.

RANK_REASON The cluster contains an academic paper detailing a new finding about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

LLMs use "direction of ignorance" to temper Bayesian priors

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The cluster contains an academic paper detailing a new finding about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Toni J. B. Liu, Jiajun Bao, Yizhou Liu, Gurbir Arora, Nicolas Boull\'e, Rapha\"el Sarfati, Christopher J. Earls ·

    The Geometry of Ignorance: LLMs Know When to Temper Bayesian Priors

    arXiv:2609.02959v1 Announce Type: cross Abstract: What does a language model predict when it has few clues? The answer lurks in its unembedding geometry: a single direction of the unembedding matrix encodes the unigram distribution of the training corpus, which serves as the Baye…

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