🏢 College of Computer Science and Technology, Jilin University
Semi-supervised Multi-label Learning with Balanced Binary Angular Margin Loss
·1724 words·9 mins·
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Semi-Supervised Learning
🏢 College of Computer Science and Technology, Jilin University
S2ML2-BBAM: A new semi-supervised multi-label learning method that balances feature angle distributions to improve accuracy and fairness.
Instance-adaptive Zero-shot Chain-of-Thought Prompting
·2222 words·11 mins·
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Natural Language Processing
Large Language Models
🏢 College of Computer Science and Technology, Jilin University
Instance-adaptive prompting significantly improves zero-shot Chain-of-Thought reasoning in LLMs by dynamically selecting prompts tailored to each instance, leading to consistent performance gains acro…