416 citations · 1k across the 55 of their papers we have counts for
13 papers · 1 filter
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency
Zilin Xiao, Linjun Shou, Xingyao Zhang +4
Previous entity disambiguation (ED) methods adopt a discriminative paradigm, where prediction is made based on matching scores between mention context and candidate entities using…
Instructed Language Models with Retrievers Are Powerful Entity Linkers
Zilin Xiao, Ming Gong, Jie Wu +4
Generative approaches powered by large language models (LLMs) have demonstrated emergent abilities in tasks that require complex reasoning abilities. Yet the generative nature stil…
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
Nuo Chen, Zinan Zheng, Ning Wu +3
Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in pre…
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Ning Wu, Ming Gong, Linjun Shou +2
Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately un…
DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory
Shengming Yin, Chenfei Wu, Jian Liang +4
Controllable video generation has gained significant attention in recent years. However, two main limitations persist: Firstly, most existing works focus on either text, image, or…
Alleviating Over-smoothing for Unsupervised Sentence Representation
Nuo Chen, Linjun Shou, Ming Gong +5
Currently, learning better unsupervised sentence representations is the pursuit of many natural language processing communities. Lots of approaches based on pre-trained language mo…
Typos-aware Bottlenecked Pre-Training for Robust Dense Retrieval
Shengyao Zhuang, Linjun Shou, Jian Pei +4
Current dense retrievers (DRs) are limited in their ability to effectively process misspelled queries, which constitute a significant portion of query traffic in commercial search…
TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Yaobo Liang, Chenfei Wu, Ting Song +11
Artificial Intelligence (AI) has made incredible progress recently. On the one hand, advanced foundation models like ChatGPT can offer powerful conversation, in-context learning an…
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
Houxing Ren, Linjun Shou, Ning Wu +2
In monolingual dense retrieval, lots of works focus on how to distill knowledge from cross-encoder re-ranker to dual-encoder retriever and these methods achieve better performance…
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
Houxing Ren, Linjun Shou, Jian Pei +3
Recent multilingual pre-trained models have shown better performance in various multilingual tasks. However, these models perform poorly on multilingual retrieval tasks due to lack…
Large Language Models are Diverse Role-Players for Summarization Evaluation
Ning Wu, Ming Gong, Linjun Shou +2
Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluat…
Bridge the Gap between Language models and Tabular Understanding
Nuo Chen, Linjun Shou, Ming Gong +5
Table pretrain-then-finetune paradigm has been proposed and employed at a rapid pace after the success of pre-training in the natural language domain. Despite the promising finding…
Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval
Shunyu Zhang, Yaobo Liang, Ming Gong +2
Recently multi-lingual pre-trained language models (PLM) such as mBERT and XLM-R have achieved impressive strides in cross-lingual dense retrieval. Despite its successes, they are…