51 citations · 113 across the 9 of their papers we have counts for
10 papers
Characterizing tradeoffs between teaching via language and demonstrations in multi-agent systems
Dhara Yu, Noah D. Goodman, Jesse Mu
Humans teach others about the world through language and demonstration. When might one of these modalities be more effective than the other? In this work, we study the factors that…
Learning to Compress Prompts with Gist Tokens
Jesse Mu, Xiang Lisa Li, Noah Goodman
Prompting is the primary way to utilize the multitask capabilities of language models (LMs), but prompts occupy valuable space in the input context window, and repeatedly encoding…
Improving Policy Learning via Language Dynamics Distillation
Victor Zhong, Jesse Mu, Luke Zettlemoyer +2
Recent work has shown that augmenting environments with language descriptions improves policy learning. However, for environments with complex language abstractions, learning how t…
Active Learning Helps Pretrained Models Learn the Intended Task
Alex Tamkin, Dat Nguyen, Salil Deshpande +2
Models can fail in unpredictable ways during deployment due to task ambiguity, when multiple behaviors are consistent with the provided training data. An example is an object class…
Improving Intrinsic Exploration with Language Abstractions
Jesse Mu, Victor Zhong, Roberta Raileanu +4
Reinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their envi…
Calibrate your listeners! Robust communication-based training for pragmatic speakers
Rose E. Wang, Julia White, Jesse Mu +1
To be good conversational partners, natural language processing (NLP) systems should be trained to produce contextually useful utterances. Prior work has investigated training NLP…