Unverified50% confidenceFactExact time
KLD depends on the choice of reference model and calibration dataset, so values from different experiments cannot be directly compared.
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Long-term
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11/2/2026
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VerifiedKL divergence is used to compare the difference in output probability distributions between the fine-tuned model and the original model given the same input; a larger value indicates greater deviation, and a KLD of 0.0244 means the fine-tuning has hardly changed the model's overall behavioral distribution.72% similarUnverifiedKLD is a distribution-level average metric and does not strictly correspond monotonically to downstream task accuracy; a model with slightly higher KLD may happen to score better on a certain type of task.68% similarUnverifiedThe research team supplemented the data with synthetic and contrastive samples to help the model distinguish cases that resemble dataset citations but are not, thereby reducing false positives.66% similarUnverifiedJudging whether a quantized model is suitable for a specific task ultimately still requires evaluation in real scenarios and cannot rely on KLD alone.65% similarUnverifiedModel capability cannot be judged by parameter scale alone, as model performance is influenced by multiple factors including training data distribution, reasoning pathways, and randomness.65% similar
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