Unverified50% confidenceOpinionExact time
When multiple models run in parallel, hallucinations often manifest as divergent answers rather than simple right or wrong, so they cannot be resolved by 'majority voting'.
1
Sources
50%
Confidence
Long-term
Relevance
10/23/2026
First Seen
Sources
Lawyer Xiao Fang's AI Soulmate: Legal AI in Practice and Lessons for MaaS
bilibili硅基流动SiliconFlow10/23/2026
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Unverified在无法替换更大模型时，多次采样、自洽性投票和结构化思考链往往能带来立竿见影的效果74% similarUnverifiedThe premise for majority voting to yield gains is that voters make mistakes on uncorrelated things, that is, errors are approximately independent, and models from different vendors are the closest thing to independent failure samples without building one's own model.73% similarUnverifiedThe hallucination problem cannot be eradicated by increasing the number of parameters or switching to better corpora, because the probabilistic generation paradigm itself determines that deviations are inevitable.73% similarUnverified一致性检验只能降低幻觉概率，无法根除，如果所有推理路径基于同一错误的共识性偏见会共振出一致但错误的答案71% similarUnverifiedPure reasoning chains (Chain-of-Thought) are prone to hallucination because the model cannot obtain feedback from the real world.71% similar
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