9 papers · 1 filter
GIANTS: Generative Insight Anticipation from Scientific Literature
Joy He-Yueya, Anikait Singh, Ge Gao +5
Scientific breakthroughs often emerge from synthesizing prior ideas into novel contributions. While language models (LMs) show promise in scientific discovery, their ability to per…
Human-like Affective Cognition in Foundation Models
Kanishk Gandhi, Zoe Lynch, Jan-Philipp Fränken +5
Understanding emotions is fundamental to human interaction and experience. Humans easily infer emotions from situations or facial expressions, situations from emotions, and do a va…
Learning to Simulate Human Dialogue
Kanishk Gandhi, Agam Bhatia, Noah D. Goodman
To predict what someone will say is to model how they think. We study this through next-turn dialogue prediction: given a conversation, predict the next utterance produced by a per…
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
Kanishk Gandhi, Ayush Chakravarthy, Anikait Singh +2
Test-time inference has emerged as a powerful paradigm for enabling language models to ``think'' longer and more carefully about complex challenges, much like skilled human experts…
Non-literal Understanding of Number Words by Language Models
Polina Tsvilodub, Kanishk Gandhi, Haoran Zhao +3
Humans naturally interpret numbers non-literally, effortlessly combining context, world knowledge, and speaker intent. We investigate whether large language models (LLMs) interpret…
STaR-GATE: Teaching Language Models to Ask Clarifying Questions
Chinmaya Andukuri, Jan-Philipp Fränken, Tobias Gerstenberg +1
When prompting language models to complete a task, users often leave important aspects unsaid. While asking questions could resolve this ambiguity (GATE; Li et al., 2023), models o…