2 citations · 2 across the 3 of their papers we have counts for
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★ 2 cited
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,…