3 papers
cs.LG2026
On Unlearning for Time-series Forecasting
Zeyu Shi, Yanhui Luo, Ziming Hong +4
Time-series forecasting is widely used in sensitive domains. Models in these settings are often trained on longitudinal user- or entity-level records, which may later require remov…
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…