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cs.LG2025
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…
cs.CL2025★ 1 cited
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…