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University of Electronic Science and Technology of China
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Removing misleading reasoning from answer-correct CoT traces can drastically enhance fine-tuning performance in LLMs.
Conformal prediction offers a surprisingly effective way to handle both modality imbalance and noisy corruption in multimodal learning by explicitly modeling predictive uncertainty during training.
