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Evidence Integration in Large Language Models
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evidence integrationlarge language modelsreasoningmachine learning
2609.04290
Builder Relevance
7h ago80%
Abstract
This paper explores how large language models (LLMs) integrate external evidence into their decision-making processes.
Reality Card
Core Claim
The study presents a distributional theory of evidence integration in LLMs, demonstrating that evidence can shift the distribution of initial answers based on receiver properties rather than just trust in the evidence source.
Method / Result
Confirmed predictions over ten million trials across twelve LLMs from four families and eight domains.
Limitations
The paper does not specify the authors, which may limit reproducibility and further exploration of the findings.
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