MACR adaptively assesses LLM confidence via semantic entropy then applies inductive multi-agent reasoning with rule-induction, conflict-analysis, and resolution agents to handle unreliable parametric and contextual knowledge.
On the Risk of Misinformation Pollution with Large Language Models
3 Pith papers cite this work. Polarity classification is still indexing.
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Experiments on four models and three datasets show SFT increases sensitivity to easy contexts while later stages (DPO, RLVR) can reinforce or reverse those preferences depending on the dataset.
LLMs propagate misinformation more in lower-resource languages and lower-HDI countries, with input safety classifiers and retrieval-augmented fact-checking showing cross-lingual and regional gaps.
citing papers explorer
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Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
MACR adaptively assesses LLM confidence via semantic entropy then applies inductive multi-agent reasoning with rule-induction, conflict-analysis, and resolution agents to handle unreliable parametric and contextual knowledge.
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Emergence of Context Characteristics Sensitivity in Large Language Models
Experiments on four models and three datasets show SFT increases sensitivity to easy contexts while later stages (DPO, RLVR) can reinforce or reverse those preferences depending on the dataset.
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To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs
LLMs propagate misinformation more in lower-resource languages and lower-HDI countries, with input safety classifiers and retrieval-augmented fact-checking showing cross-lingual and regional gaps.