62 citations · 205 across the 27 of their papers we have counts for
4 papers · 1 filter
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…
Ranking with Long-Term Constraints
Kianté Brantley, Zhichong Fang, Sarah Dean +1
The feedback that users provide through their choices (e.g., clicks, purchases) is one of the most common types of data readily available for training search and recommendation alg…
Learning to Generate Better Than Your LLM
Jonathan D. Chang, Kiante Brantley, Rajkumar Ramamurthy +2
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT and GPT-…
Interactive Text Generation
Felix Faltings, Michel Galley, Baolin Peng +5
Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between…