1 citations · 1 across the 2 of their papers we have counts for
4 papers
A Simple "Try Again" Can Elicit Multi-Turn LLM Reasoning
Licheng Liu, Zihan Wang, Linjie Li +5
Multi-turn problem solving is critical yet challenging for Large Reasoning Models (LRMs) to reflect on their reasoning and revise from feedback. Existing Reinforcement Learning (RL…
LLM-based Text Simplification and its Effect on User Comprehension and Cognitive Load
Theo Guidroz, Diego Ardila, Jimmy Li +24
Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a L…
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
An appropriate choice of batch sizes in large-scale model training is crucial, yet it involves an intrinsic yet inevitable dilemma: large-batch training improves training efficienc…
Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu +2
We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of an…