Large language models (LLMs) have been widely used in various applicatio...
Multimodal Entity Linking (MEL) is the task of mapping mentions with
mul...
As ChatGPT and GPT-4 spearhead the development of Large Language Models
...
Conditional inference on joint textual and visual clues is a multi-modal...
Training a Large Visual Language Model (LVLM) from scratch, like GPT-4, ...
Pretrained Vision-Language Models (VLMs) have achieved remarkable perfor...
Multi-modal and multi-hop question answering aims to answer a question b...
Calibration strengthens the trustworthiness of black-box models by produ...
Pre-trained Language Models (PLMs) have been applied in NLP tasks and ac...
Access to external knowledge is essential for many natural language
proc...
Modern Entity Linking (EL) systems entrench a popularity bias, yet there...
Visual Entailment with natural language explanations aims to infer the
r...
Recent works have shown explainability and robustness are two crucial
in...
Graph convolutional network (GCN) has become popular in various natural
...
Neural predictive models have achieved groundbreaking performance
improv...
Neural data-to-text generation models have achieved significant advancem...
Neural image inpainting has achieved promising performance in generating...
Sentence simplification aims to simplify the content and structure of co...
Coherence plays a critical role in producing a high-quality summary from...
In this paper, we propose a novel model, named Stroke Sequence-dependent...
In this paper, the answer selection problem in community question answer...
Automatic text summarization is widely regarded as the highly difficult
...
Semantic matching is of central importance to many natural language task...
We propose a novel method for translation selection in statistical machi...