300 citations · 963 across the 33 of their papers we have counts for
33 papers
Good Questions Help Zero-Shot Image Reasoning
Kaiwen Yang, Tao Shen, Xinmei Tian +4
Aligning the recent large language models (LLMs) with computer vision models leads to large vision-language models (LVLMs), which have paved the way for zero-shot image reasoning t…
Thread of Thought Unraveling Chaotic Contexts
Yucheng Zhou, Xiubo Geng, Tao Shen +4
Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation. Neve…
Re-Reading Improves Reasoning in Large Language Models
Xiaohan Xu, Chongyang Tao, Tao Shen +5
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i.e., \textbf{Re}-\text…
Federated Prompt Learning for Weather Foundation Models on Devices
Shengchao Chen, Guodong Long, Tao Shen +2
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting hu…
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Jiazhan Feng, Chongyang Tao, Xiubo Geng +5
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to mode…
Large Language Models are Strong Zero-Shot Retriever
Tao Shen, Guodong Long, Xiubo Geng +3
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retri…
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
Ziyang luo, Pu Zhao, Can Xu +6
Image-text retrieval (ITR) is a task to retrieve the relevant images/texts, given the query from another modality. The conventional dense retrieval paradigm relies on encoding imag…
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data
Shengchao Chen, Guodong Long, Tao Shen +1
To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urge…
Fine-Grained Distillation for Long Document Retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng +4
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a…
Adam: Dense Retrieval Distillation with Adaptive Dark Examples
Chongyang Tao, Chang Liu, Tao Shen +4
To improve the performance of the dual-encoder retriever, one effective approach is knowledge distillation from the cross-encoder ranker. Existing works construct the candidate pas…
CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification
Yang Li, Canran Xu, Guodong Long +3
Recently, prefix-tuning was proposed to efficiently adapt pre-trained language models to a broad spectrum of natural language classification tasks. It leverages soft prefix as task…
LexMAE: Lexicon-Bottlenecked Pretraining for Large-Scale Retrieval
Tao Shen, Xiubo Geng, Chongyang Tao +5
In large-scale retrieval, the lexicon-weighting paradigm, learning weighted sparse representations in vocabulary space, has shown promising results with high quality and low latenc…
LED: Lexicon-Enlightened Dense Retriever for Large-Scale Retrieval
Kai Zhang, Chongyang Tao, Tao Shen +4
Retrieval models based on dense representations in semantic space have become an indispensable branch for first-stage retrieval. These retrievers benefit from surging advances in r…
Towards Robust Ranker for Text Retrieval
Yucheng Zhou, Tao Shen, Xiubo Geng +5
A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxi…
KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP
Yufei Wang, Jiayi Zheng, Can Xu +4
This paper focuses on the data augmentation for low-resource NLP tasks where the training set is limited. The existing solutions either leverage task-independent heuristic rules (e…
UnifieR: A Unified Retriever for Large-Scale Retrieval
Tao Shen, Xiubo Geng, Chongyang Tao +4
Large-scale retrieval is to recall relevant documents from a huge collection given a query. It relies on representation learning to embed documents and queries into a common semant…
ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification
Yucheng Zhou, Tao Shen, Xiubo Geng +2
Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks. Existing works either limit their scope to specific scenarios o…
PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings
Qiyu Wu, Chongyang Tao, Tao Shen +3
Learning sentence embeddings in an unsupervised manner is fundamental in natural language processing. Recent common practice is to couple pre-trained language models with unsupervi…
EventBERT: A Pre-Trained Model for Event Correlation Reasoning
Yucheng Zhou, Xiubo Geng, Tao Shen +2
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense. For example, "Andrew was very drowsy, so he took…
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision
Yang Li, Guodong Long, Tao Shen +1
Distant supervision uses triple facts in knowledge graphs to label a corpus for relation extraction, leading to wrong labeling and long-tail problems. Some works use the hierarchy…
Sequential Diagnosis Prediction with Transformer and Ontological Representation
Xueping Peng, Guodong Long, Tao Shen +2
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain. EHR data, sequential records of a pati…
Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction
Bo Wang, Tao Shen, Guodong Long +2
Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence. ALSC is a practical setting in aspect-based sentiment a…
Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health
Guodong Long, Tao Shen, Yue Tan +3
Privacy protection is an ethical issue with broad concern in Artificial Intelligence (AI). Federated learning is a new machine learning paradigm to learn a shared model across user…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent cl…