17 citations · 57 across the 17 of their papers we have counts for
17 papers
Adam's Law: Textual Frequency Law on Large Language Models
Hongyuan Adam Lu, Z. L., Victor Wei +5
While textual frequency has been validated as relevant to human cognition in reading speed, its relatedness to Large Language Models (LLMs) is seldom studied. We propose a novel re…
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…
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…
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…
EcomGPT-CT: Continual Pre-training of E-commerce Large Language Models with Semi-structured Data
Shirong Ma, Shen Huang, Shulin Huang +6
Large Language Models (LLMs) pre-trained on massive corpora have exhibited remarkable performance on various NLP tasks. However, applying these models to specific domains still pos…
EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Yangning Li, Shirong Ma, Xiaobin Wang +6
Recently, instruction-following Large Language Models (LLMs) , represented by ChatGPT, have exhibited exceptional performance in general Natural Language Processing (NLP) tasks. Ho…
LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzles
Shulin Huang, Shirong Ma, Yinghui Li +4
With the continuous evolution and refinement of LLMs, they are endowed with impressive logical reasoning or vertical thinking capabilities. But can they think out of the box? Do th…
On the (In)Effectiveness of Large Language Models for Chinese Text Correction
Yinghui Li, Haojing Huang, Shirong Ma +5
Recently, the development and progress of Large Language Models (LLMs) have amazed the entire Artificial Intelligence community. Benefiting from their emergent abilities, LLMs have…
Correct Like Humans: Progressive Learning Framework for Chinese Text Error Correction
Yinghui Li, Shirong Ma, Shaoshen Chen +5
Chinese Text Error Correction (CTEC) aims to detect and correct errors in the input text, which benefits human daily life and various downstream tasks. Recent approaches mainly emp…
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
Jingheng Ye, Yinghui Li, Qingyu Zhou +4
Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possi…
From Retrieval to Generation: Efficient and Effective Entity Set Expansion
Shulin Huang, Shirong Ma, Yangning Li +2
Entity Set Expansion (ESE) is a critical task aiming at expanding entities of the target semantic class described by seed entities. Most existing ESE methods are retrieval-based fr…
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation
Shulin Huang, Shirong Ma, Yinghui Li +4
Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG res…
Focus Is What You Need For Chinese Grammatical Error Correction
Jingheng Ye, Yinghui Li, Shirong Ma +3
Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text. In the long term, researchers regard CGEC as a ta…
Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
Shirong Ma, Yinghui Li, Rongyi Sun +9
Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the…
Learning from the Dictionary: Heterogeneous Knowledge Guided Fine-tuning for Chinese Spell Checking
Yinghui Li, Shirong Ma, Qingyu Zhou +7
Chinese Spell Checking (CSC) aims to detect and correct Chinese spelling errors. Recent researches start from the pretrained knowledge of language models and take multimodal inform…
Contextual Similarity is More Valuable than Character Similarity: An Empirical Study for Chinese Spell Checking
Ding Zhang, Yinghui Li, Qingyu Zhou +4
Chinese Spell Checking (CSC) task aims to detect and correct Chinese spelling errors. Recently, related researches focus on introducing character similarity from confusion set to e…
Equality before the Law: Legal Judgment Consistency Analysis for Fairness
Yuzhong Wang, Chaojun Xiao, Shirong Ma +5
In a legal system, judgment consistency is regarded as one of the most important manifestations of fairness. However, due to the complexity of factual elements that impact sentenci…