4 citations · 6 across the 4 of their papers we have counts for
4 papers
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…
Program-Aided Reasoners (better) Know What They Know
Anubha Kabra, Sanketh Rangreji, Yash Mathur +3
Prior work shows that program-aided reasoning, in which large language models (LLMs) are combined with programs written in programming languages such as Python, can significantly i…
Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
Simon Stepputtis, Joseph Campbell, Yaqi Xie +6
Deception and persuasion play a critical role in long-horizon dialogues between multiple parties, especially when the interests, goals, and motivations of the participants are not…
SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarization
Yash Mathur, Sanketh Rangreji, Raghav Kapoor +3
Medical dialogue summarization is challenging due to the unstructured nature of medical conversations, the use of medical terminology in gold summaries, and the need to identify ke…