55 citations · 61 across the 30 of their papers we have counts for
10 papers · 1 filter
Schoenfeld's Anatomy of Mathematical Reasoning by Language Models
Ming Li, Chenrui Fan, Yize Cheng +2
Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statist…
Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction
Ming Li, Han Chen, Yunze Xiao +3
Accurate estimation of item (question or task) difficulty is critical for educational assessment but suffers from the cold start problem. While Large Language Models demonstrate su…
V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions
Chenrui Fan, Yijun Liang, Shweta Bhardwaj +3
While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle i…
Automated Alignment of Math Items to Content Standards in Large-Scale Assessments Using Language Models
Qingshu Xu, Hong Jiao, Tianyi Zhou +4
Accurate alignment of items to content standards is critical for valid score interpretation in large-scale assessments. This study evaluates three automated paradigms for aligning…
Text-Based Approaches to Item Alignment to Content Standards in Large-Scale Reading & Writing Tests
Yanbin Fu, Hong Jiao, Tianyi Zhou +5
Aligning test items to content standards is a critical step in test development to collect validity evidence based on content. Item alignment has typically been conducted by human…
Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
Sydney Peters, Nan Zhang, Hong Jiao +3
Item difficulty plays a crucial role in test performance, interpretability of scores, and equity for all test-takers, especially in large-scale assessments. Traditional approaches…
Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory
Ming Li, Nan Zhang, Chenrui Fan +6
While Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, we lack a principled framework for understanding how these thoughts are structured. In this paper…
VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding
Jian Chen, Ming Li, Jihyung Kil +6
Most organizational data in this world are stored as documents, and visual retrieval plays a crucial role in unlocking the collective intelligence from all these documents. However…
What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding
Ming Li, Zhengyuan Yang, Xiyao Wang +4
Large reasoning models (LRMs) achieve strong reasoning performance by emitting long chains of thought. Yet, these verbose traces slow down inference and often drift into unnecessar…
Towards Visual Text Grounding of Multimodal Large Language Model
Ming Li, Ruiyi Zhang, Jian Chen +7
Despite the existing evolution of Multimodal Large Language Models (MLLMs), a non-neglectable limitation remains in their struggle with visual text grounding, especially in text-ri…