2 citations · 2 across the 7 of their papers we have counts for
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cs.CL2024
Aligning LLMs with Individual Preferences via Interaction
Shujin Wu, May Fung, Cheng Qian +3
As large language models (LLMs) demonstrate increasingly advanced capabilities, aligning their behaviors with human values and preferences becomes crucial for their wide adoption.…
cs.CL2024
MACAROON: Training Vision-Language Models To Be Your Engaged Partners
Shujin Wu, Yi R. Fung, Sha Li +3
Large vision-language models (LVLMs), while proficient in following instructions and responding to diverse questions, invariably generate detailed responses even when questions are…
cs.CL2024★ 2 cited
SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales
Tianyang Xu, Shujin Wu, Shizhe Diao +4
Large language models (LLMs) often generate inaccurate or fabricated information and generally fail to indicate their confidence, which limits their broader applications. Previous…