9 citations · 9 across the 6 of their papers we have counts for
6 papers
From Weights to Words: Expressing and Editing Preference Model Inferences in Natural Language
Zachary Wojtowicz, Ayush Nayak, Jacob Andreas
The growing use of statistical learning algorithms to infer human preferences from high-dimensional choice data runs up against a fundamental challenge: choice alternatives typical…
Training Language Models to Explain Their Own Computations
Belinda Z. Li, Zifan Carl Guo, Vincent Huang +2
Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs'…
(How) Do Language Models Track State?
Belinda Z. Li, Zifan Carl Guo, Jacob Andreas
Transformer language models (LMs) exhibit behaviors -- from storytelling to code generation -- that seem to require tracking the unobserved state of an evolving world. How do they…
Bayesian Preference Elicitation with Language Models
Kunal Handa, Yarin Gal, Ellie Pavlick +4
Aligning AI systems to users' interests requires understanding and incorporating humans' complex values and preferences. Recently, language models (LMs) have been used to gather in…
Interpreting User Requests in the Context of Natural Language Standing Instructions
Nikita Moghe, Patrick Xia, Jacob Andreas +3
Users of natural language interfaces, generally powered by Large Language Models (LLMs),often must repeat their preferences each time they make a similar request. We describe an ap…
Eliciting Human Preferences with Language Models
Belinda Z. Li, Alex Tamkin, Noah Goodman +1
Language models (LMs) can be directed to perform target tasks by using labeled examples or natural language prompts. But selecting examples or writing prompts for can be challengin…