Search Results for author: Baotian Hu

Found 47 papers, 21 papers with code

Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text

no code implementations EMNLP 2020 Dongfang Li, Baotian Hu, Qingcai Chen, Weihua Peng, Anqi Wang

Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.

Machine Reading Comprehension

SelectIT: Selective Instruction Tuning for Large Language Models via Uncertainty-Aware Self-Reflection

1 code implementation26 Feb 2024 Liangxin Liu, Xuebo Liu, Derek F. Wong, Dongfang Li, Ziyi Wang, Baotian Hu, Min Zhang

In this work, we propose a novel approach, termed SelectIT, that capitalizes on the foundational capabilities of the LLM itself.

Does the Generator Mind its Contexts? An Analysis of Generative Model Faithfulness under Context Transfer

no code implementations22 Feb 2024 Xinshuo Hu, Baotian Hu, Dongfang Li, Xiaoguang Li, Lifeng Shang

The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context.

Generative Question Answering Hallucination +1

Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment

no code implementations21 Feb 2024 Yunxin Li, Xinyu Chen, Baotian Hu, Haoyuan Shi, Min Zhang

Evaluating and Rethinking the current landscape of Large Multimodal Models (LMMs), we observe that widely-used visual-language projection approaches (e. g., Q-former or MLP) focus on the alignment of image-text descriptions yet ignore the visual knowledge-dimension alignment, i. e., connecting visuals to their relevant knowledge.

Language Modelling Question Answering +1

A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation

no code implementations21 Feb 2024 Yunxin Li, Baotian Hu, Wenhan Luo, Lin Ma, Yuxin Ding, Min Zhang

This approach preserves the language generation prowess of large language models (LLMs), facilitating a substantial increase in description diversity.

In-Context Learning Language Modelling +2

Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training

no code implementations29 Dec 2023 Dongfang Li, Baotian Hu, Qingcai Chen, Shan He

Feature attribution methods highlight the important input tokens as explanations to model predictions, which have been widely applied to deep neural networks towards trustworthy AI.

text-classification Text Classification

Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs

no code implementations27 Nov 2023 Yunxin Li, Baotian Hu, Wei Wang, Xiaochun Cao, Min Zhang

These models predominantly map visual information into language representation space, leveraging the vast knowledge and powerful text generation abilities of LLMs to produce multimodal instruction-following responses.

Instruction Following multimodal generation +1

Temporal Knowledge Question Answering via Abstract Reasoning Induction

no code implementations15 Nov 2023 Ziyang Chen, Dongfang Li, Xiang Zhao, Baotian Hu, Min Zhang

In this paper, we tackle the significant challenge of temporal knowledge reasoning in Large Language Models (LLMs), an area where such models frequently encounter difficulties.

Question Answering

Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration

1 code implementation14 Nov 2023 Zhenran Xu, Senbao Shi, Baotian Hu, Jindi Yu, Dongfang Li, Min Zhang, Yuxiang Wu

Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks.

Math

A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering

no code implementations13 Nov 2023 Yunxin Li, Longyue Wang, Baotian Hu, Xinyu Chen, Wanqi Zhong, Chenyang Lyu, Wei Wang, Min Zhang

The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answering (VQA).

Decision Making General Knowledge +3

A Survey of Large Language Models Attribution

1 code implementation7 Nov 2023 Dongfang Li, Zetian Sun, Xinshuo Hu, Zhenyu Liu, Ziyang Chen, Baotian Hu, Aiguo Wu, Min Zhang

Open-domain generative systems have gained significant attention in the field of conversational AI (e. g., generative search engines).

Revisiting Sparse Retrieval for Few-shot Entity Linking

1 code implementation19 Oct 2023 Yulin Chen, Zhenran Xu, Baotian Hu, Min Zhang

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base.

Entity Linking Retrieval

A Read-and-Select Framework for Zero-shot Entity Linking

1 code implementation19 Oct 2023 Zhenran Xu, Yulin Chen, Baotian Hu, Min Zhang

Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.

Entity Disambiguation Entity Linking +1

Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation

1 code implementation16 Aug 2023 Xinshuo Hu, Dongfang Li, Baotian Hu, Zihao Zheng, Zhenyu Liu, Min Zhang

To evaluate the effectiveness of our approach in terms of truthfulness and detoxification, we conduct extensive experiments on LLMs, encompassing additional abilities such as language modeling and mathematical reasoning.

Language Modelling Mathematical Reasoning

Generative Multimodal Entity Linking

1 code implementation22 Jun 2023 Senbao Shi, Zhenran Xu, Baotian Hu, Min Zhang

Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base (e. g. Wikipedia).

Entity Linking In-Context Learning +1

ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination

1 code implementation22 May 2023 Dongfang Li, Jindi Yu, Baotian Hu, Zhenran Xu, Min Zhang

As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks.

General Knowledge In-Context Learning

A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues

1 code implementation8 May 2023 Yunxin Li, Baotian Hu, Xinyu Chen, Yuxin Ding, Lin Ma, Min Zhang

This makes the language model well-suitable for such multi-modal reasoning scenario on joint textual and visual clues.

Language Modelling

LMEye: An Interactive Perception Network for Large Language Models

1 code implementation5 May 2023 Yunxin Li, Baotian Hu, Xinyu Chen, Lin Ma, Yong Xu, Min Zhang

LMEye addresses this issue by allowing the LLM to request the desired visual information aligned with various human instructions, which we term as the dynamic visual information interaction.

Language Modelling Large Language Model +1

A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text

1 code implementation3 May 2023 Yunxin Li, Baotian Hu, Yuxin Ding, Lin Ma, Min Zhang

Inspired by the Divide-and-Conquer algorithm and dual-process theory, in this paper, we regard linguistically complex texts as compound proposition texts composed of multiple simple proposition sentences and propose an end-to-end Neural Divide-and-Conquer Reasoning framework, dubbed NDCR.

Image Retrieval Logical Reasoning +1

Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation

1 code implementation16 Dec 2022 Qian Yang, Qian Chen, Wen Wang, Baotian Hu, Min Zhang

Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low.

Answer Generation Language Modelling +3

Prompt-based Text Entailment for Low-Resource Named Entity Recognition

no code implementations COLING 2022 Dongfang Li, Baotian Hu, Qingcai Chen

To address these challenges, we propose Prompt-based Text Entailment (PTE) for low-resource named entity recognition, which better leverages knowledge in the PLMs.

Low Resource Named Entity Recognition named-entity-recognition +3

Calibration Meets Explanation: A Simple and Effective Approach for Model Confidence Estimates

1 code implementation6 Nov 2022 Dongfang Li, Baotian Hu, Qingcai Chen

We conduct extensive experiments on six datasets with two popular pre-trained language models in the in-domain and out-of-domain settings.

An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks

1 code implementation30 Oct 2022 Yuxiang Wu, Yu Zhao, Baotian Hu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel

Experiments on various knowledge-intensive tasks such as question answering and dialogue datasets show that, simply augmenting parametric models (T5-base) using our method produces more accurate results (e. g., 25. 8 -> 44. 3 EM on NQ) while retaining a high throughput (e. g., 1000 queries/s on NQ).

Computational Efficiency Question Answering +1

Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark

1 code implementation26 Jul 2022 Zhenran Xu, Zifei Shan, Yuxin Li, Baotian Hu, Bing Qin

We then establish a strong baseline that scores a R@1 of 46. 2% on Few-Shot and 76. 6% on Zero-Shot on our dataset.

Entity Linking

MSDF: A General Open-Domain Multi-Skill Dialog Framework

no code implementations17 Jun 2022 Yu Zhao, Xinshuo Hu, Yunxin Li, Baotian Hu, Dongfang Li, Sichao Chen, Xiaolong Wang

In this paper, we propose a general Multi-Skill Dialog Framework, namely MSDF, which can be applied in different dialog tasks (e. g. knowledge grounded dialog and persona based dialog).

Medical Dialogue Response Generation with Pivotal Information Recalling

no code implementations17 Jun 2022 Yu Zhao, Yunxin Li, Yuxiang Wu, Baotian Hu, Qingcai Chen, Xiaolong Wang, Yuxin Ding, Min Zhang

To mitigate this problem, we propose a medical response generation model with Pivotal Information Recalling (MedPIR), which is built on two components, i. e., knowledge-aware dialogue graph encoder and recall-enhanced generator.

Dialogue Generation Graph Attention +1

Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction

1 code implementation20 Dec 2021 Dongfang Li, Baotian Hu, Qingcai Chen, Tujie Xu, Jingcong Tao, Yunan Zhang

Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.

text-classification Text Classification

Sentence-level Online Handwritten Chinese Character Recognition

no code implementations4 Jul 2021 Yunxin Li, Qian Yang, Qingcai Chen, Lin Ma, Baotian Hu, Xiaolong Wang, Yuxin Ding

Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance.

Sentence Word Embeddings

GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph

no code implementations1 Jul 2021 Yunxin Li, Yu Zhao, Baotian Hu, Qingcai Chen, Yang Xiang, Xiaolong Wang, Yuxin Ding, Lin Ma

Previous works indicate that the glyph of Chinese characters contains rich semantic information and has the potential to enhance the representation of Chinese characters.

Multi-hop Graph Convolutional Network with High-order Chebyshev Approximation for Text Reasoning

1 code implementation ACL 2021 Shuoran Jiang, Qingcai Chen, Xin Liu, Baotian Hu, Lisai Zhang

In this study, we define the spectral graph convolutional network with the high-order dynamic Chebyshev approximation (HDGCN), which augments the multi-hop graph reasoning by fusing messages aggregated from direct and long-term dependencies into one convolutional layer.

You Can Do Better! If You Elaborate the Reason When Making Prediction

no code implementations27 Mar 2021 Dongfang Li, Jingcong Tao, Qingcai Chen, Baotian Hu

The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus.

Multiple-choice Natural Language Inference +1

MedWriter: Knowledge-Aware Medical Text Generation

no code implementations COLING 2020 Youcheng Pan, Qingcai Chen, Weihua Peng, Xiaolong Wang, Baotian Hu, Xin Liu, Junying Chen, Wenxiu Zhou

To exploit the domain knowledge to guarantee the correctness of generated text has been a hot topic in recent years, especially for high professional domains such as medical.

Text Generation

Neural Data-to-Text Generation with Dynamic Content Planning

no code implementations16 Apr 2020 Kai Chen, Fayuan Li, Baotian Hu, Weihua Peng, Qingcai Chen, Hong Yu

We further design a reconstruction mechanism with a novel objective function that can reconstruct the whole entry of the used data sequentially from the hidden states of the decoder, which aids the accuracy of the generated text.

Data-to-Text Generation

Decomposing Word Embedding with the Capsule Network

no code implementations7 Apr 2020 Xin Liu, Qingcai Chen, Yan Liu, Joanna Siebert, Baotian Hu, Xiang-Ping Wu, Buzhou Tang

We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S.

Binary Classification Word Embeddings +1

Text-Guided Neural Image Inpainting

1 code implementation7 Apr 2020 Lisai Zhang, Qingcai Chen, Baotian Hu, Shuoran Jiang

To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet).

Descriptive Image Inpainting +4

Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning

no code implementations WS 2019 Dongfang Li, Ying Xiong, Baotian Hu, Hanyang Du, Buzhou Tang, Qingcai Chen

In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.

Drug Discovery Multi-Task Learning +3

Sentence Simplification with Memory-Augmented Neural Networks

no code implementations NAACL 2018 Tu Vu, Baotian Hu, Tsendsuren Munkhdalai, Hong Yu

Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications.

Machine Translation Sentence +2

Learning to Extract Coherent Summary via Deep Reinforcement Learning

no code implementations19 Apr 2018 Yuxiang Wu, Baotian Hu

As an effort towards extracting coherent summaries, we propose a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns.

Extractive Summarization Feature Engineering +4

Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition

no code implementations13 Oct 2016 Baotian Hu, Xin Liu, Xiang-Ping Wu, Qingcai Chen

In this paper, we propose a novel model, named Stroke Sequence-dependent Deep Convolutional Neural Network (SSDCNN), using the stroke sequence information and eight-directional features for Online Handwritten Chinese Character Recognition (OLHCCR).

Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

no code implementations IJCNLP 2015 Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, Xiaolong Wang

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem.

Answer Selection Community Question Answering

LCSTS: A Large Scale Chinese Short Text Summarization Dataset

3 code implementations EMNLP 2015 Baotian Hu, Qingcai Chen, Fangze Zhu

Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set.

Text Summarization

Context-Dependent Translation Selection Using Convolutional Neural Network

no code implementations IJCNLP 2015 Zhaopeng Tu, Baotian Hu, Zhengdong Lu, Hang Li

We propose a novel method for translation selection in statistical machine translation, in which a convolutional neural network is employed to judge the similarity between a phrase pair in two languages.

Machine Translation Semantic Similarity +3

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