34 citations · 45 across the 11 of their papers we have counts for
11 papers
TUBench: Benchmarking Large Vision-Language Models on Trustworthiness with Unanswerable Questions
Xingwei He, Qianru Zhang, A-Long Jin +2
Large Vision-Language Models (LVLMs) have achieved remarkable progress on visual perception and linguistic interpretation. Despite their impressive capabilities across various task…
A Survey on Point-of-Interest Recommendation: Models, Architectures, and Security
Qianru Zhang, Peng Yang, Junliang Yu +4
The widespread adoption of smartphones and Location-Based Social Networks has led to a massive influx of spatio-temporal data, creating unparalleled opportunities for enhancing Poi…
Enhancing Few-Shot Stock Trend Prediction with Large Language Models
Yiqi Deng, Xingwei He, Jiahao Hu +1
The goal of stock trend prediction is to forecast future market movements for informed investment decisions. Existing methods mostly focus on predicting stock trends with supervise…
A Survey of Generative Techniques for Spatial-Temporal Data Mining
Qianru Zhang, Haixin Wang, Cheng Long +8
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. Wi…
Improving Factual Error Correction by Learning to Inject Factual Errors
Xingwei He, Qianru Zhang, A-Long Jin +3
Factual error correction (FEC) aims to revise factual errors in false claims with minimal editing, making them faithful to the provided evidence. This task is crucial for alleviati…
Noisy Pair Corrector for Dense Retrieval
Hang Zhang, Yeyun Gong, Xingwei He +4
Most dense retrieval models contain an implicit assumption: the training query-document pairs are exactly matched. Since it is expensive to annotate the corpus manually, training p…
AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators
Xingwei He, Zhenghao Lin, Yeyun Gong +7
Many natural language processing (NLP) tasks rely on labeled data to train machine learning models with high performance. However, data annotation is time-consuming and expensive,…
CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion
Xingwei He, Yeyun Gong, A-Long Jin +5
The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing t…
Metric-guided Distillation: Distilling Knowledge from the Metric to Ranker and Retriever for Generative Commonsense Reasoning
Xingwei He, Yeyun Gong, A-Long Jin +7
Commonsense generation aims to generate a realistic sentence describing a daily scene under the given concepts, which is very challenging, since it requires models to have relation…
Parallel Refinements for Lexically Constrained Text Generation with BART
Xingwei He
Lexically constrained text generation aims to control the generated text by incorporating some pre-specified keywords into the output. Previous work injects lexical constraints int…
Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet
Xingwei He, Victor O. K. Li
Lexically constrained sentence generation allows the incorporation of prior knowledge such as lexical constraints into the output. This technique has been applied to machine transl…