From the 1 of 6 linked papers with an AI index.
6 papers
Language Models Encode the Contextual Truth of Propositions
Rupak Sarkar, Pritika Ramu, Rachel Rudinger
Prior work has shown that LLMs encode the truth of factual propositions along linear directions in activation space. It's unclear how these representations extend to contextual tru…
Sycophancy Undermines Epistemic Vigilance in Cooperative Vision-Language Tasks
Rupak Sarkar, Neha Srikanth, Saloni Gupta +3
To maintain common ground in cooperative conversation, humans iteratively update their beliefs as conversation participants share new information; participants who are epistemicall…
(Im)Paired Programming: Coding Agents Improve Productivity but Harm Understanding
Nishant Balepur, Connor Baumler, Valerie Chen +3
The study finds that AI coding assistants help students finish a programming task faster but reduce their understanding of the code, making it harder for them to extend it later.
Reheat Nachos for Dinner? Evaluating AI Support for Cross-Cultural Communication of Neologisms
Dayeon Ki, Yu Hou, Rachel Rudinger +3
Neologisms and emerging slang are central to daily conversation, yet challenging for non-native speakers (NNS) to interpret and use appropriately in cross-cultural communication wi…
DRACULA: Hunting for the Actions Users Want Deep Research Agents to Execute
Nishant Balepur, Malachi Hamada, Varsha Kishore +9
Scientific Deep Research (DR) agents answer user queries by synthesizing research papers into multi-section reports. User feedback can improve their utility, but existing protocols…
DiscoTrace: Representing and Comparing Answering Strategies of Humans and LLMs in Information-Seeking Question Answering
Neha Srikanth, Jordan Boyd-Graber, Rachel Rudinger
We introduce DiscoTrace, a method to identify the rhetorical strategies that answerers use when responding to information-seeking questions. DiscoTrace represents answers as a sequ…