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cs.CL2024★ 1 cited
In-context Demonstration Matters: On Prompt Optimization for Pseudo-Supervision Refinement
Zhen-Yu Zhang, Jiandong Zhang, Huaxiu Yao +2
Large language models (LLMs) have achieved great success across diverse tasks, and fine-tuning is sometimes needed to further enhance generation quality. Most existing methods rely…
cs.LG2024
Dual-Decoupling Learning and Metric-Adaptive Thresholding for Semi-Supervised Multi-Label Learning
Jia-Hao Xiao, Ming-Kun Xie, Heng-Bo Fan +3
Semi-supervised multi-label learning (SSMLL) is a powerful framework for leveraging unlabeled data to reduce the expensive cost of collecting precise multi-label annotations. Unlik…
cs.LG2024
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
Qizhou Wang, Bo Han, Puning Yang +3
The imperative to eliminate undesirable data memorization underscores the significance of machine unlearning for large language models (LLMs). Recent research has introduced a seri…