activity
20202026
most citedA Survey of Natural Language Generation

151 citations · 229 across the 44 of their papers we have counts for

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Showing 2024Show all

9 papers · 1 filter

cs.CL2024

Loss-Aware Curriculum Learning for Chinese Grammatical Error Correction

Ding Zhang, Yangning Li, Lichen Bai +6

Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to imp…

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★ 2 cited

Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

Yangning Li, Yinghui Li, Xinyu Wang +8

Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs). Although prom…

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★ 1 cited

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark

Zhikun Xu, Yinghui Li, Ruixue Ding +7

How to better evaluate the capabilities of Large Language Models (LLMs) is the focal point and hot topic in current LLMs research. Previous work has noted that due to the extremely…

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★ 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…

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…