25 citations · 96 across the 12 of their papers we have counts for
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cs.AI2024
AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents
Zhe Su, Xuhui Zhou, Sanketh Rangreji +4
Truthfulness (adherence to factual accuracy) and utility (satisfying human needs and instructions) are both fundamental aspects of Large Language Models, yet these goals often conf…
cs.AI2024★ 17 cited
Prompt Design Matters for Computational Social Science Tasks but in Unpredictable Ways
Shubham Atreja, Joshua Ashkinaze, Lingyao Li +2
Manually annotating data for computational social science tasks can be costly, time-consuming, and emotionally draining. While recent work suggests that LLMs can perform such annot…