6 citations · 12 across the 8 of their papers we have counts for
4 papers · 1 filter
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
Yueqin Yin, Shentao Yang, Yujia Xie +5
Reinforcement learning from human feedback (RLHF) has been widely adopted to align language models (LMs) with human preference. Prior RLHF works typically take a bandit formulation…
Sequential Decision-Making for Inline Text Autocomplete
Rohan Chitnis, Shentao Yang, Alborz Geramifard
Autocomplete suggestions are fundamental to modern text entry systems, with applications in domains such as messaging and email composition. Typically, autocomplete suggestions are…
Preference-grounded Token-level Guidance for Language Model Fine-tuning
Shentao Yang, Shujian Zhang, Congying Xia +3
Aligning language models (LMs) with preferences is an important problem in natural language generation. A key challenge is that preferences are typically provided at the sequence l…
Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems
Yihao Feng, Shentao Yang, Shujian Zhang +4
When learning task-oriented dialogue (ToD) agents, reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve user-specific goals. Pri…