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20232025
most citedSTaR-GATE: Teaching Language Models to Ask Clarifying Questions

5 citations · 7 across the 8 of their papers we have counts for

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cs.CL2025

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…

cs.CL2024★ 1 cited

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…

cs.CL2024

PERSONA: A Reproducible Testbed for Pluralistic Alignment

Louis Castricato, Nathan Lile, Rafael Rafailov +2

The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…

cs.CL2024★ 1 cited

Procedural Dilemma Generation for Evaluating Moral Reasoning in Humans and Language Models

Jan-Philipp Fränken, Kanishk Gandhi, Tori Qiu +3

As AI systems like language models are increasingly integrated into decision-making processes affecting people's lives, it's critical to ensure that these systems have sound moral…

cs.CL2024★ 5 cited

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…