136 citations · 563 across the 31 of their papers we have counts for
6 papers · 2 filters
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…
Leveraging Implicit Feedback from Deployment Data in Dialogue
Richard Yuanzhe Pang, Stephen Roller, Kyunghyun Cho +2
We study improving social conversational agents by learning from natural dialogue between users and a deployed model, without extra annotations. To implicitly measure the quality o…
Efficient Shapley Values Estimation by Amortization for Text Classification
Chenghao Yang, Fan Yin, He He +3
Despite the popularity of Shapley Values in explaining neural text classification models, computing them is prohibitive for large pretrained models due to a large number of model e…
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
Chenglei Si, Dan Friedman, Nitish Joshi +3
In-context learning (ICL) is an important paradigm for adapting large language models (LLMs) to new tasks, but the generalization behavior of ICL remains poorly understood. We inve…
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…
How do decoding algorithms distribute information in dialogue responses?
Saranya Venkatraman, He He, David Reitter
Humans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID p…