activity
20232026
most citedWhen LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

2 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding

Yinghui Li, Jiayi Kuang, Peng Xing +11

Multimodal large language models (MLLMs) perform strongly on natural images, yet their ability to understand discrete visual symbols remains unclear. We present a multi-domain benc…

cs.CL2024

CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction

Jingheng Ye, Zishan Xu, Yinghui Li +9

The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we intro…

cs.CL2024★ 2 cited

When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

Yinghui Li, Qingyu Zhou, Yuanzhen Luo +5

Recently, Large Language Models (LLMs) make remarkable evolutions in language understanding and generation. Following this, various benchmarks for measuring all kinds of capabiliti…

cs.CL2023★ 2 cited

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters

Yinghui Li, Zishan Xu, Shaoshen Chen +7

Writing assistance is an application closely related to human life and is also a fundamental Natural Language Processing (NLP) research field. Its aim is to improve the correctness…

cs.CL2023★ 1 cited

A Frustratingly Easy Plug-and-Play Detection-and-Reasoning Module for Chinese Spelling Check

Haojing Huang, Jingheng Ye, Qingyu Zhou +4

In recent years, Chinese Spelling Check (CSC) has been greatly improved by designing task-specific pre-training methods or introducing auxiliary tasks, which mostly solve this task…