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20182025
most citedCJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

53 citations · 107 across the 9 of their papers we have counts for

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23 papers · 1 filter

cs.CL2023

IDOL: Indicator-oriented Logic Pre-training for Logical Reasoning

Zihang Xu, Ziqing Yang, Yiming Cui +1

In the field of machine reading comprehension (MRC), existing systems have surpassed the average performance of human beings in many tasks like SQuAD. However, there is still a lon…

cs.CL2023

JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving

Wayne Xin Zhao, Kun Zhou, Beichen Zhang +8

Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver,…

cs.CL20239 cited

Evaluating and Improving Tool-Augmented Computation-Intensive Math Reasoning

Beichen Zhang, Kun Zhou, Xilin Wei +4

Chain-of-thought prompting~(CoT) and tool augmentation have been validated in recent work as effective practices for improving large language models~(LLMs) to perform step-by-step…

cs.CL20231 cited

CSED: A Chinese Semantic Error Diagnosis Corpus

Bo Sun, Baoxin Wang, Yixuan Wang +4

Recently, much Chinese text error correction work has focused on Chinese Spelling Check (CSC) and Chinese Grammatical Error Diagnosis (CGED). In contrast, little attention has been…

cs.CL20234 cited

MiniRBT: A Two-stage Distilled Small Chinese Pre-trained Model

Xin Yao, Ziqing Yang, Yiming Cui +1

In natural language processing, pre-trained language models have become essential infrastructures. However, these models often suffer from issues such as large size, long inference…

cs.CL202226 cited

LERT: A Linguistically-motivated Pre-trained Language Model

Yiming Cui, Wanxiang Che, Shijin Wang +1

Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training…