4 citations · 12 across the 46 of their papers we have counts for
5 papers · 1 filter
Process Supervision of Confidence Margin for Calibrated LLM Reasoning
Liaoyaqi Wang, Chunsheng Zuo, William Jurayj +2
Scaling test-time computation with reinforcement learning (RL) has emerged as a reliable path to improve large language models (LLM) reasoning ability. Yet, outcome-based reward of…
Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting
Michael Y. Hu, Benjamin Van Durme, Jacob Andreas +1
Language model (LM) agents deployed in novel environments often exhibit poor sample efficiency when learning from sequential interactions. This significantly hinders the usefulness…
Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass
Tong Chen, Hao Fang, Patrick Xia +5
Large language models (LMs) are typically adapted to improve performance on new contexts (\eg text prompts that define new tasks or domains) through fine-tuning or prompting. Howev…
Baby Bear: Seeking a Just Right Rating Scale for Scalar Annotations
Xu Han, Felix Yu, Joao Sedoc +1
Our goal is a mechanism for efficiently assigning scalar ratings to each of a large set of elements. For example, "what percent positive or negative is this product review?" When s…
AdapterSwap: Continuous Training of LLMs with Data Removal and Access-Control Guarantees
William Fleshman, Aleem Khan, Marc Marone +1
Large language models (LLMs) are increasingly capable of completing knowledge intensive tasks by recalling information from a static pretraining corpus. Here we are concerned with…