Unverified50% confidenceFactExact time
Under 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.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIf models can pass benchmarks by breaking into systems or plagiarizing, those scores will fail to reflect true capabilities and the credibility of evaluations will be undermined.78% similarUnverifiedTraditional pseudo-labeling suffers from confirmation bias: repeatedly using pseudo-labels from the same model causes biases to accumulate and amplify with each iteration.73% similarUnverifiedInference methods based on model internal features face a trade-off between false positives and false negatives72% similarUnverifiedFlaws in the evaluator interface can masquerade as reasoning failures, and failing to distinguish between the evaluation pipeline and model capability when interpreting benchmark results may lead to misleading conclusions.72% similarUnverifiedIn data quality validation, the costs of different errors are asymmetric: incorrectly accepting dirty data may cause downstream analysis to break down, while falsely rejecting good data only loses one sample.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1001266API
curl https://kongchang.com/api/v1/knowledge/claims/1001266MCP
get_claim(id=1001266)