301 citations · 558 across the 13 of their papers we have counts for
14 papers
MIEB: Massive Image Embedding Benchmark
Chenghao Xiao, Isaac Chung, Imene Kerboua +7
Image representations are often evaluated through disjointed, task-specific protocols, leading to a fragmented understanding of model capabilities. For instance, it is unclear whet…
Towards Text-Image Interleaved Retrieval
Xin Zhang, Ziqi Dai, Yongqi Li +7
Current multimodal information retrieval studies mainly focus on single-image inputs, which limits real-world applications involving multiple images and text-image interleaved cont…
GME: Improving Universal Multimodal Retrieval by Multimodal LLMs
Xin Zhang, Yanzhao Zhang, Wen Xie +7
Universal Multimodal Retrieval (UMR) aims to enable search across various modalities using a unified model, where queries and candidates can consist of pure text, images, or a comb…
mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval
Xin Zhang, Yanzhao Zhang, Dingkun Long +10
We present systematic efforts in building long-context multilingual text representation model (TRM) and reranker from scratch for text retrieval. We first introduce a text encoder…
Language Models are Universal Embedders
Xin Zhang, Zehan Li, Yanzhao Zhang +4
In the large language model (LLM) revolution, embedding is a key component of various systems, such as retrieving knowledge or memories for LLMs or building content moderation filt…
Towards General Text Embeddings with Multi-stage Contrastive Learning
Zehan Li, Xin Zhang, Yanzhao Zhang +3
We present GTE, a general-purpose text embedding model trained with multi-stage contrastive learning. In line with recent advancements in unifying various NLP tasks into a single f…
Holistic Exploration on Universal Decompositional Semantic Parsing: Architecture, Data Augmentation, and LLM Paradigm
Hexuan Deng, Xin Zhang, Meishan Zhang +2
In this paper, we conduct a holistic exploration of the Universal Decompositional Semantic (UDS) Parsing. We first introduce a cascade model for UDS parsing that decomposes the com…
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
Xuming Hu, Aiwei Liu, Zeqi Tan +4
Relation extraction (RE) tasks show promising performance in extracting relations from two entities mentioned in sentences, given sufficient annotations available during training.…
ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
Xiang Wei, Xingyu Cui, Ning Cheng +9
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text. It is challenging due to involving little human intervention. Challenging but worthwhile,…
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Biyang Guo, Xin Zhang, Ziyuan Wang +5
The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions…
Extending Phrase Grounding with Pronouns in Visual Dialogues
Panzhong Lu, Xin Zhang, Meishan Zhang +1
Conventional phrase grounding aims to localize noun phrases mentioned in a given caption to their corresponding image regions, which has achieved great success recently. Apparently…
Domain-Specific NER via Retrieving Correlated Samples
Xin Zhang, Yong Jiang, Xiaobin Wang +4
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requir…
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Xin Zhang, Guangwei Xu, Yueheng Sun +3
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to sat…
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named Entity Recognition
Xin Zhang, Guangwei Xu, Yueheng Sun +2
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focu…