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20242026
most citedRethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

4 citations · 6 across the 11 of their papers we have counts for

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11 papers

cs.AI2026

From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI

Yongheng Zhang, Ziang Liu, Jiaxuan Zhu +17

Large Language Models (LLMs) are undergoing a fundamental transformation from conversational generators into integrated AI systems capable of reasoning, action, memory, and self-im…

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.CL2025

AdmTree: Compressing Lengthy Context with Adaptive Semantic Trees

Yangning Li, Shaoshen Chen, Yinghui Li +5

The quadratic complexity of self-attention constrains Large Language Models (LLMs) in processing long contexts, a capability essential for many advanced applications. Context compr…

cs.CL2025

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning

Yangning Li, Tingwei Lu, Yinghui Li +6

Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) trained on a given instruction dataset. Curriculum learning as a typical data…

cs.CL2025

RAISE: Reinforced Adaptive Instruction Selection For Large Language Models

Qingsong Lv, Yangning Li, Zihua Lan +8

In the instruction fine-tuning of large language models (LLMs), it is widely recognized that a few high-quality instructions are superior to a large number of low-quality instructi…

cs.CL2024

Exploring the Implicit Semantic Ability of Multimodal Large Language Models: A Pilot Study on Entity Set Expansion

Hebin Wang, Yangning Li, Yinghui Li +3

The rapid development of multimodal large language models (MLLMs) has brought significant improvements to a wide range of tasks in real-world applications. However, LLMs still exhi…

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★ 1 cited

EXCGEC: A Benchmark for Edit-Wise Explainable Chinese Grammatical Error Correction

Jingheng Ye, Shang Qin, Yinghui Li +8

Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited scenario, where they ignore the interaction between corrections and explanations and…

cs.CL2024

UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities

Yangning Li, Qingsong Lv, Tianyu Yu +5

Entity Set Expansion (ESE) aims to identify new entities belonging to the same semantic class as the given set of seed entities. Traditional methods solely relied on positive seed…

cs.CL2024★ 1 cited

Mitigating Catastrophic Forgetting in Multi-domain Chinese Spelling Correction by Multi-stage Knowledge Transfer Framework

Peng Xing, Yinghui Li, Shirong Ma +6

Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers bec…

cs.CL2024★ 4 cited

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

Yinghui Li, Shang Qin, Haojing Huang +6

Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Gram…