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LLM overconfidence linked to knowledge popularity, new study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the phenomenon of large language models (LLMs) exhibiting high confidence in incorrect answers, a problem termed overconfidence. The study, focusing on knowledge popularity, found that hallucinated answers are often more popular or frequently associated with the question entity than correct ones. Furthermore, LLMs tend to assign higher confidence to more popular answers, even when they are wrong. The research proposes that incorporating knowledge popularity signals can significantly mitigate this overconfidence, reducing average confidence on incorrect answers and improving overall confidence estimation. AI

IMPACT Suggests methods to improve LLM reliability and trustworthiness by addressing overconfidence in generated responses.

RANK_REASON Academic paper detailing a new finding about LLM behavior and proposing a mitigation strategy. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

LLM overconfidence linked to knowledge popularity, new study finds

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Academic paper detailing a new finding about LLM behavior and proposing a mitigation strategy. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shiyu Ni, Keping Bi, Jiafeng Guo, Xueqi Cheng ·

    Popular but Wrong: Understanding and Mitigating LLM Overconfidence through Knowledge Popularity

    arXiv:2505.17537v2 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect answers with high confidence, yet the factors associated with such overconfidence remain insufficiently understood. We study this problem through the lens of knowledge popular…

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