565 citations · 1.3k across the 39 of their papers we have counts for
39 papers
ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understanding
Xingqi Wang, Yiming Cui, Xin Yao +3
Large Vision-Language Models (LVLMs) have recently demonstrated remarkable progress, yet hallucination remains a critical barrier, particularly in chart understanding, which requir…
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…
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,…
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…
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…
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…
Gradient-based Intra-attention Pruning on Pre-trained Language Models
Ziqing Yang, Yiming Cui, Xin Yao +1
Pre-trained language models achieve superior performance but are computationally expensive. Techniques such as pruning and knowledge distillation have been developed to reduce thei…
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…
Overview of CTC 2021: Chinese Text Correction for Native Speakers
Honghong Zhao, Baoxin Wang, Dayong Wu +3
In this paper, we present an overview of the CTC 2021, a Chinese text correction task for native speakers. We give detailed descriptions of the task definition and the data for tra…
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding
Wayne Xin Zhao, Kun Zhou, Zheng Gong +7
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model~(PLM) for effectively understanding and…
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training
Ziqing Yang, Yiming Cui, Zhigang Chen +1
Multilingual pre-trained language models (MPLMs) not only can handle tasks in different languages but also exhibit surprising zero-shot cross-lingual transferability. However, MPLM…
InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities
Baoxin Wang, Qingye Meng, Ziyue Wang +6
Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction…
Multilingual Multi-Aspect Explainability Analyses on Machine Reading Comprehension Models
Yiming Cui, Wei-Nan Zhang, Wanxiang Che +3
Achieving human-level performance on some of the Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs).…
Adversarial Training for Machine Reading Comprehension with Virtual Embeddings
Ziqing Yang, Yiming Cui, Chenglei Si +4
Adversarial training (AT) as a regularization method has proved its effectiveness on various tasks. Though there are successful applications of AT on some NLP tasks, the distinguis…
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual Transfer
Ziqing Yang, Wentao Ma, Yiming Cui +3
Multilingual pre-trained models have achieved remarkable performance on cross-lingual transfer learning. Some multilingual models such as mBERT, have been pre-trained on unlabeled…
ExpMRC: Explainability Evaluation for Machine Reading Comprehension
Yiming Cui, Ting Liu, Wanxiang Che +2
Achieving human-level performance on some of Machine Reading Comprehension (MRC) datasets is no longer challenging with the help of powerful Pre-trained Language Models (PLMs). How…
Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering
Nan Shao, Yiming Cui, Ting Liu +2
Retrieving information from correlative paragraphs or documents to answer open-domain multi-hop questions is very challenging. To deal with this challenge, most of the existing wor…
Unsupervised Explanation Generation for Machine Reading Comprehension
Yiming Cui, Ting Liu, Shijin Wang +1
With the blooming of various Pre-trained Language Models (PLMs), Machine Reading Comprehension (MRC) has embraced significant improvements on various benchmarks and even surpass hu…
CharBERT: Character-aware Pre-trained Language Model
Wentao Ma, Yiming Cui, Chenglei Si +3
Most pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almos…
Conversational Word Embedding for Retrieval-Based Dialog System
Wentao Ma, Yiming Cui, Ting Liu +3
Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR…
Benchmarking Robustness of Machine Reading Comprehension Models
Chenglei Si, Ziqing Yang, Yiming Cui +3
Machine Reading Comprehension (MRC) is an important testbed for evaluating models' natural language understanding (NLU) ability. There has been rapid progress in this area, with ne…
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Yiming Cui, Wanxiang Che, Ting Liu +3
Bidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further imp…
A Sentence Cloze Dataset for Chinese Machine Reading Comprehension
Yiming Cui, Ting Liu, Ziqing Yang +5
Owing to the continuous efforts by the Chinese NLP community, more and more Chinese machine reading comprehension datasets become available. To add diversity in this area, in this…
Is Graph Structure Necessary for Multi-hop Question Answering?
Nan Shao, Yiming Cui, Ting Liu +2
Recently, attempting to model texts as graph structure and introducing graph neural networks to deal with it has become a trend in many NLP research areas. In this paper, we invest…