35 citations · 104 across the 27 of their papers we have counts for
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cs.CL2024★ 1 cited
Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models
Xinran Zhao, Hongming Zhang, Xiaoman Pan +4
For a LLM to be trustworthy, its confidence level should be well-calibrated with its actual performance. While it is now common sense that LLM performances are greatly impacted by…
cs.LG2024
Rewards-in-Context: Multi-objective Alignment of Foundation Models with Dynamic Preference Adjustment
Rui Yang, Xiaoman Pan, Feng Luo +4
We consider the problem of multi-objective alignment of foundation models with human preferences, which is a critical step towards helpful and harmless AI systems. However, it is g…