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Found 49 papers, 23 papers with code
Glyph Features Matter: A Multimodal Solution for EvaHan in LT4HALA2022
We participate in the LT4HALA2022 shared task EvaHan.
Towards Medical Machine Reading Comprehension with Structural Knowledge and Plain Text
Machine reading comprehension (MRC) has achieved significant progress on the open domain in recent years, mainly due to large-scale pre-trained language models.
In-Context Learning State Vector with Inner and Momentum Optimization
In this paper, we address this gap by presenting a comprehensive analysis of these compressed vectors, drawing parallels to the parameters trained with gradient descent, and introduce the concept of state vector.
Improving Attributed Text Generation of Large Language Models via Preference Learning
Large language models have been widely adopted in natural language processing, yet they face the challenge of generating unreliable content.
SelectIT: Selective Instruction Tuning for Large Language Models via Uncertainty-Aware Self-Reflection
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
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.
Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment
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.
A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation
For this setting, previous methods utilize visual and textual encoders to encode the image and keywords and employ a language model-based decoder to generate the product description.
Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training
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.
Towards Vision Enhancing LLMs: Empowering Multimodal Knowledge Storage and Sharing in LLMs
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.
Temporal Knowledge Question Answering via Abstract Reasoning Induction
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.
Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration
Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks.
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
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).
A Survey of Large Language Models Attribution
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
Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base.
A Read-and-Select Framework for Zero-shot Entity Linking
Zero-shot entity linking (EL) aims at aligning entity mentions to unseen entities to challenge the generalization ability.
Separate the Wheat from the Chaff: Model Deficiency Unlearning via Parameter-Efficient Module Operation
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.
Generative Multimodal Entity Linking
Multimodal Entity Linking (MEL) is the task of mapping mentions with multimodal contexts to the referent entities from a knowledge base.
ExplainCPE: A Free-text Explanation Benchmark of Chinese Pharmacist Examination
As ChatGPT and GPT-4 spearhead the development of Large Language Models (LLMs), more researchers are investigating their performance across various tasks.
A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues
This makes the language model well-suitable for such multi-modal reasoning scenario on joint textual and visual clues.
LMEye: An Interactive Perception Network for Large Language Models
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.
A Neural Divide-and-Conquer Reasoning Framework for Image Retrieval from Linguistically Complex Text
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.
Enhancing Multi-modal and Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation
Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low.
Prompt-based Text Entailment for Low-Resource Named Entity Recognition
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
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
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).
Ranked #4 on
Question Answering
on KILT: ELI5
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
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.
Chunk-aware Alignment and Lexical Constraint for Visual Entailment with Natural Language Explanations
CSI), a relation inferrer, and a Lexical Constraint-aware Generator (arr.
Medical Dialogue Response Generation with Pivotal Information Recalling
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.
MSDF: A General Open-Domain Multi-Skill Dialog Framework
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).
Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction
Recent works have shown explainability and robustness are two crucial ingredients of trustworthy and reliable text classification.
Sentence-level Online Handwritten Chinese Character Recognition
Single online handwritten Chinese character recognition~(single OLHCCR) has achieved prominent performance.
GlyphCRM: Bidirectional Encoder Representation for Chinese Character with its Glyph
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
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
The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus.
Transforming Recurrent Neural Networks with Attention and Fixed-point Equations
It has two major parts.
MedWriter: Knowledge-Aware Medical Text Generation
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.
Neural Data-to-Text Generation with Dynamic Content Planning
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.
Decomposing Word Embedding with the Capsule Network
We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S.
Text-Guided Neural Image Inpainting
To fulfill such a task, we propose a novel inpainting model named Text-Guided Dual Attention Inpainting Network (TDANet).
Trigger Word Detection and Thematic Role Identification via BERT and Multitask Learning
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.
Sentence Simplification with Memory-Augmented Neural Networks
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.
Ranked #1 on
Text Simplification
on PWKP / WikiSmall
Learning to Extract Coherent Summary via Deep Reinforcement Learning
As an effort towards extracting coherent summaries, we propose a neural coherence model to capture the cross-sentence semantic and syntactic coherence patterns.
Ranked #3 on
Text Summarization
on CNN / Daily Mail (Anonymized)
Stroke Sequence-Dependent Deep Convolutional Neural Network for Online Handwritten Chinese Character Recognition
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
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.
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
Automatic text summarization is widely regarded as the highly difficult problem, partially because of the lack of large text summarization data set.
Ranked #1 on
Text Summarization
on LCSTS
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic, RetrievalQA}.
Ranked #3 on
Question Answering
on SemEvalCQA
Context-Dependent Translation Selection Using Convolutional Neural Network
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.
