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cs.CL2025
Fine-Tuned LLMs Know They Don't Know: A Parameter-Efficient Approach to Recovering Honesty
Zeyu Shi, Ziming Wang, Tianyu Chen +4
The honesty of Large Language Models (LLMs) is increasingly important for safe deployment in high-stakes domains. However, this crucial trait is severely undermined by supervised f…
cs.CL2025
Towards Objective Fine-tuning: How LLMs' Prior Knowledge Causes Potential Poor Calibration?
Ziming Wang, Zeyu Shi, Haoyi Zhou +3
Fine-tuned Large Language Models (LLMs) often demonstrate poor calibration, with their confidence scores misaligned with actual performance. While calibration has been extensively…
cs.CL2024
MIKO: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery
Feihong Lu, Weiqi Wang, Yangyifei Luo +8
Social media has become a ubiquitous tool for connecting with others, staying updated with news, expressing opinions, and finding entertainment. However, understanding the intentio…