17 citations · 70 across the 20 of their papers we have counts for
20 papers
Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference
Zhuo Chen, Xinyu Wang, Yong Jiang +5
Despite the advancements made in Vision Large Language Models (VLLMs), like text Large Language Models (LLMs), they have limitations in addressing questions that require real-time…
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models
Zhen Zhang, Xinyu Wang, Yong Jiang +7
Large Language Models (LLMs) often struggle with dynamically changing knowledge and handling unknown static information. Retrieval-Augmented Generation (RAG) is employed to tackle…
Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent
Yangning Li, Yinghui Li, Xinyu Wang +8
Multimodal Retrieval Augmented Generation (mRAG) plays an important role in mitigating the "hallucination" issue inherent in multimodal large language models (MLLMs). Although prom…
RaFe: Ranking Feedback Improves Query Rewriting for RAG
Shengyu Mao, Yong Jiang, Boli Chen +7
As Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved, query rewriting has been widely incorporated into the RAG system for downstream…
Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts
Zhuo Chen, Xinyu Wang, Yong Jiang +3
In the era of large language models, applying techniques such as Retrieval Augmented Generation can better address Open-Domain Question-Answering problems. Due to constraints inclu…
Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark
Zhikun Xu, Yinghui Li, Ruixue Ding +7
How to better evaluate the capabilities of Large Language Models (LLMs) is the focal point and hot topic in current LLMs research. Previous work has noted that due to the extremely…
Named Entity and Relation Extraction with Multi-Modal Retrieval
Xinyu Wang, Jiong Cai, Yong Jiang +3
Multi-modal named entity recognition (NER) and relation extraction (RE) aim to leverage relevant image information to improve the performance of NER and RE. Most existing efforts l…
DAMO-NLP at SemEval-2022 Task 11: A Knowledge-based System for Multilingual Named Entity Recognition
Xinyu Wang, Yongliang Shen, Jiong Cai +9
The MultiCoNER shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of contexts makes…
ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition
Xinyu Wang, Min Gui, Yong Jiang +6
Recently, Multi-modal Named Entity Recognition (MNER) has attracted a lot of attention. Most of the work utilizes image information through region-level visual representations obta…
Enhanced Universal Dependency Parsing with Automated Concatenation of Embeddings
Xinyu Wang, Zixia Jia, Yong Jiang +1
This paper describes the system used in submission from SHANGHAITECH team to the IWPT 2021 Shared Task. Our system is a graph-based parser with the technique of Automated Concatena…
Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Recent advances in Named Entity Recognition (NER) show that document-level contexts can significantly improve model performance. In many application scenarios, however, such contex…
AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network
Xinyu Wang, Yong Jiang, Nguyen Bach +4
The linear-chain Conditional Random Field (CRF) model is one of the most widely-used neural sequence labeling approaches. Exact probabilistic inference algorithms such as the forwa…
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor
Xinyu Wang, Yong Jiang, Zhaohui Yan +6
Knowledge distillation is a critical technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). The object…
Automated Concatenation of Embeddings for Structured Prediction
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Pretrained contextualized embeddings are powerful word representations for structured prediction tasks. Recent work found that better word representations can be obtained by concat…
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
Xinyu Wang, Kewei Tu
In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the…
More Embeddings, Better Sequence Labelers?
Xinyu Wang, Yong Jiang, Nguyen Bach +4
Recent work proposes a family of contextual embeddings that significantly improves the accuracy of sequence labelers over non-contextual embeddings. However, there is no definite c…
Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
Xinyu Wang, Yong Jiang, Kewei Tu
This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}. Our system is a graph-based parser with second-order inference. For the low-resource Ta…
Structure-Level Knowledge Distillation For Multilingual Sequence Labeling
Xinyu Wang, Yong Jiang, Nguyen Bach +3
Multilingual sequence labeling is a task of predicting label sequences using a single unified model for multiple languages. Compared with relying on multiple monolingual models, us…
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
Xinyu Wang, Yixian Liu, Zixia Jia +2
This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}. Our system is a graph-based parser whi…
Second-Order Semantic Dependency Parsing with End-to-End Neural Networks
Xinyu Wang, Jingxian Huang, Kewei Tu
Semantic dependency parsing aims to identify semantic relationships between words in a sentence that form a graph. In this paper, we propose a second-order semantic dependency pars…