5 citations · 10 across the 6 of their papers we have counts for
6 papers
Inverse RL Helps Align AI by Imitating Humans
Michał Wiliński, Liu Leqi, Chirag Nagpal
Language model alignment aims to make model behavior reflect desirable properties such as helpfulness, safety, and instruction following. Existing methods draw supervision from exp…
Filtered Reasoning Score: Evaluating Reasoning Quality on a Model's Most-Confident Traces
Manas Pathak, Xingyao Chen, Shuozhe Li +2
Should we trust Large Language Models (LLMs) with high accuracy? LLMs achieve high accuracy on reasoning benchmarks, but correctness alone does not reveal the quality of the reason…
A Unified Causal Framework for Auditing Recommender Systems for Ethical Concerns
Vibhhu Sharma, Shantanu Gupta, Nil-Jana Akpinar +2
As recommender systems become widely deployed in different domains, they increasingly influence their users' beliefs and preferences. Auditing recommender systems is crucial as it…
Personalized Language Modeling from Personalized Human Feedback
Xinyu Li, Ruiyang Zhou, Zachary C. Lipton +1
Personalized large language models (LLMs) are designed to tailor responses to individual user preferences. While Reinforcement Learning from Human Feedback (RLHF) is a commonly use…
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
Charvi Rastogi, Liu Leqi, Kenneth Holstein +1
Hybrid human-ML systems increasingly make consequential decisions in a wide range of domains. These systems are often introduced with the expectation that the combined human-ML sys…
Sample Complexity of Nonparametric Semi-Supervised Learning
Chen Dan, Liu Leqi, Bryon Aragam +2
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the tru…