565 citations · 653 across the 28 of their papers we have counts for
6 papers · 1 filter
Debate Helps Supervise Unreliable Experts
Julian Michael, Salsabila Mahdi, David Rein +4
As AI systems are used to answer more difficult questions and potentially help create new knowledge, judging the truthfulness of their outputs becomes more difficult and more impor…
Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writers
Tuhin Chakrabarty, Vishakh Padmakumar, Faeze Brahman +1
The development of large language models (LLMs) capable of following instructions and engaging in conversational interactions sparked increased interest in their utilization across…
Does Writing with Language Models Reduce Content Diversity?
Vishakh Padmakumar, He He
Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of…
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar +4
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity. Recent studies ha…
Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs
Angelica Chen, Jason Phang, Alicia Parrish +4
Large language models (LLMs) have achieved widespread success on a variety of in-context few-shot tasks, but this success is typically evaluated via correctness rather than consist…
Extrapolative Controlled Sequence Generation via Iterative Refinement
Vishakh Padmakumar, Richard Yuanzhe Pang, He He +1
We study the problem of extrapolative controlled generation, i.e., generating sequences with attribute values beyond the range seen in training. This task is of significant importa…