Unverified50% confidenceOpinionExact time
The value of pseudo-labels lies not in being imitated as ground truth, but in serving as an evaluation signal that guides the model's exploration.
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50%
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Long-term
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10/26/2026
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UnverifiedUnder domain mismatch conditions, relying solely on confidence-based filtering is insufficient to guarantee pseudo-label quality, because models may also exhibit high confidence on incorrect samples.74% similarUnverified在关系抽取中，标签出现的频率几乎不能预测模型在该标签上的表现，真正决定泛化能力的是模型是否见过相似的证据表达方式73% similarUnverifiedThe risk of pseudo-labels lies in confirmation bias—erroneous predictions get reinforced; therefore in practice novelty detection (e.g., Mahalanobis distance) is usually combined to judge whether the distribution has drifted, and pseudo-label updates are enabled only when the drift is significant and prediction confidence is high.71% similarUnverifiedTraditional pseudo-labeling suffers from confirmation bias: repeatedly using pseudo-labels from the same model causes biases to accumulate and amplify with each iteration.69% similarUnverifiedAgainst self-generated prompt injection, traditional input filtering and source whitelisting defenses are nearly ineffective, requiring a redesign of validation mechanisms for model-generated content at the agent architecture level.68% similar
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