1 citations · 1 across the 4 of their papers we have counts for
5 papers
Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets
Shuze Daniel Liu, Claire Chen, Jiuqi Wang +2
Autonomous large language model (LLM) agents operating in multi-product markets must make sequential decisions under information asymmetry and resource constraints. We develop a ma…
Prompt Optimization with Logged Bandit Data
Haruka Kiyohara, Daniel Yiming Cao, Yuta Saito +1
We study how to use naturally available user feedback, such as clicks, to optimize large language model (LLM) pipelines for generating personalized sentences using prompts. Naive a…
Poor Alignment and Steerability of Large Language Models: Evidence from College Admission Essays
Jinsook Lee, AJ Alvero, Thorsten Joachims +1
People are increasingly using technologies equipped with large language models (LLM) to write texts for formal communication, which raises two important questions at the intersecti…
MultiScale Contextual Bandits for Long Term Objectives
Richa Rastogi, Yuta Saito, Thorsten Joachims
The feedback that AI systems (e.g., recommender systems, chatbots) collect from user interactions is a crucial source of training data. While short-term feedback (e.g., clicks, eng…
POTEC: Off-Policy Learning for Large Action Spaces via Two-Stage Policy Decomposition
Yuta Saito, Jihan Yao, Thorsten Joachims
We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods -- most of which rely crucially on reward-regression models…