2 citations · 3 across the 12 of their papers we have counts for
3 papers · 2 filters
Claim-Level Confidence Calibration for Reliable Decision Making with Large Language Models
Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang +1
Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is misaligned with factual correctn…
Not All Synthetic Data Is Yours to Learn From
Sina Alemohammad, Li Chen, Richard G. Baraniuk +1
Can a language model improve from plain text sampled from itself, with no prompts, no teacher, no verifier, and no reward model? Yes, but only when the synthetic corpus is compatib…
Configurable Reward Model for Balanced Safety Alignment
Zhengping Jiang, Mehran Khodabandeh, Akash Bharadwaj +5
Aligning large language models (LLMs) to heterogeneous and rapidly evolving safety requirements remains a critical challenge. Existing instruction-tuned LLMs and standalone safety…