3 citations · 9 across the 12 of their papers we have counts for
12 papers
Credit-assigned Policy Gradient for Early Stage Retrieval in Two-stage Ranking
Haruka Kiyohara, Mihaela Curmei, Ariel Evnine +7
Large-scale search, recommendation, and retrieval-augmented generation (RAG) systems typically employ a two-stage architecture: an early-stage ranker (ESR) generates a candidate se…
: Better Prompt Optimization with Fewer Prompts
Zhaolin Gao, Yu, Wang +4
Prompt optimization improves language models without updating their weights by searching for a better system prompt, but its effectiveness varies widely across tasks. We study what…
SteerEval: A Framework for Evaluating Steerability with Natural Language Profiles for Recommendation
Joyce Zhou, Weijie Zhou, Doug Turnbull +1
Natural-language user profiles have recently attracted attention not only for improved interpretability, but also for their potential to make recommender systems more steerable. By…
Prompt Curriculum Learning for Efficient LLM Post-Training
Zhaolin Gao, Joongwon Kim, Wen Sun +4
We introduce Prompt Curriculum Learning (PCL), a lightweight reinforcement learning (RL) algorithm that selects intermediate-difficulty prompts using a learned value model to post-…
End-to-end Training for Recommendation with Language-based User Profiles
Zhaolin Gao, Joyce Zhou, Yijia Dai +1
There is a growing interest in natural language-based user profiles for recommender systems, which aims to enhance transparency and scrutability compared with embedding-based metho…
Algorithms for College Admissions Decision Support: Impacts of Policy Change and Inherent Variability
Jinsook Lee, Emma Harvey, Joyce Zhou +3
Each year, selective American colleges sort through tens of thousands of applications to identify a first-year class that displays both academic merit and diversity. In the 2023-20…
REBEL: Reinforcement Learning via Regressing Relative Rewards
Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan +7
While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) application…
Language-Based User Profiles for Recommendation
Joyce Zhou, Yijia Dai, Thorsten Joachims
Most conventional recommendation methods (e.g., matrix factorization) represent user profiles as high-dimensional vectors. Unfortunately, these vectors lack interpretability and st…
Ranking with Slot Constraints
Wentao Guo, Andrew Wang, Bradon Thymes +1
We introduce the problem of ranking with slot constraints, which can be used to model a wide range of application problems -- from college admission with limited slots for differen…
Policy-Gradient Training of Language Models for Ranking
Ge Gao, Jonathan D. Chang, Claire Cardie +2
Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answer…
GPT as a Baseline for Recommendation Explanation Texts
Joyce Zhou, Thorsten Joachims
In this work, we establish a baseline potential for how modern model-generated text explanations of movie recommendations may help users, and explore what different components of t…
Fairness in Ranking under Disparate Uncertainty
Richa Rastogi, Thorsten Joachims
Ranking is a ubiquitous method for focusing the attention of human evaluators on a manageable subset of options. Its use as part of human decision-making processes ranges from surf…