3 papers
cs.CL2026
Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs
Xinyi Wang, Hong Jiao, Ming Li +4
The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores how large language models (LL…
cs.AI2025
Finding the Minimal Parameter Budget for Implicit Reasoning: A Data Complexity Driven Scaling Law for Language Models
Xinyi Wang, Shawn Tan, Shenbo Xu +4
Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pretraining. In this work, we study…
cs.CL2023
Guiding Language Model Reasoning with Planning Tokens
Xinyi Wang, Lucas Caccia, Oleksiy Ostapenko +3
Large language models (LLMs) have recently attracted considerable interest for their ability to perform complex reasoning tasks, such as chain-of-thought (CoT) reasoning. However,…