43 citations · 56 across the 21 of their papers we have counts for
22 papers
DEPT: Document Embedding Preservation Tuning for Unified Query Expansion and Retrieval
Jingyuan Wang, Richong Zhang, Zhijie Nie +2
Large language models (LLMs) can both expand underspecified queries and encode text as dense representations, suggesting a unified model for query expansion and retrieval. Existing…
UEmbed: Unified Sparse and Dense Multimodal Embeddings
Tingyu Song, Mingxin Li, Yanzhao Zhang +5
Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retrieval (LSR) to push beyond exac…
CoRect: Context-Aware Logit Contrast for Hidden State Rectification to Resolve Knowledge Conflicts
Xuhua Ma, Richong Zhang, Zhijie Nie
Retrieval-Augmented Generation (RAG) often struggles with knowledge conflicts, where model-internal parametric knowledge overrides retrieved evidence, leading to unfaithful outputs…
Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval
Zhanyu Wu, Richong Zhang, Zhijie Nie
Dense retrieval represents queries and documents as high-dimensional embeddings, but these representations can be redundant at the query level: for a given information need, only a…
AR-BENCH: Benchmarking Legal Reasoning with Judgment Error Detection, Classification and Correction
Yifei Li, Richong Zhang, Wanyu Tu +4
Legal judgments may contain errors due to the complexity of case circumstances and the abstract nature of legal concepts, while existing appellate review mechanisms face efficiency…
inversedMixup: Data Augmentation via Inverting Mixed Embeddings
Fanshuang Kong, Richong Zhang, Qiyu Sun +3
Mixup generates augmented samples by linearly interpolating inputs and labels with a controllable ratio. However, since it operates at the latent embedding level, the resulting sam…
Text2Token: Unsupervised Text Representation Learning with Token Target Prediction
Ruize An, Richong Zhang, Zhijie Nie +3
Unsupervised text representation learning (TRL) is a fundamental task in natural language processing, which is beneficial for improving search and recommendations with the web's un…
Dynamic Task Vector Grouping for Efficient Multi-Task Prompt Tuning
Pieyi Zhang, Richong Zhang, Zhijie Nie
Multi-task prompt tuning utilizes multiple high-resource source tasks to improve performance on low-source target tasks. Existing approaches transfer the soft prompt trained by com…
A Graph-based Verification Framework for Fact-Checking
Yani Huang, Richong Zhang, Zhijie Nie +2
Fact-checking plays a crucial role in combating misinformation. Existing methods using large language models (LLMs) for claim decomposition face two key limitations: (1) insufficie…
General Table Question Answering via Answer-Formula Joint Generation
Zhongyuan Wang, Richong Zhang, Zhijie Nie +1
Advanced table question answering (TableQA) methods prompt large language models (LLMs) to generate answer text, SQL query, Python code, or custom operation, which impressively imp…
MOMA: Masked Orthogonal Matrix Alignment for Zero-Additional-Parameter Model Merging
Fanshuang Kong, Richong Zhang, Zhijie Nie +4
Model merging offers a scalable alternative to multi-task learning but often yields suboptimal performance on classification tasks. We attribute this degradation to a geometric mis…
When Text Embedding Meets Large Language Model: A Comprehensive Survey
Zhijie Nie, Zhangchi Feng, Mingxin Li +4
Text embedding has become a foundational technology in natural language processing (NLP) during the deep learning era, driving advancements across a wide array of downstream tasks.…
Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging
Mingxin Li, Zhijie Nie, Yanzhao Zhang +3
Text embeddings are vital for tasks such as text retrieval and semantic textual similarity (STS). Recently, the advent of pretrained language models, along with unified benchmarks…
EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
Zhangchi Feng, Dongdong Kuang, Zhongyuan Wang +3
This paper presents EasyRAG, a simple, lightweight, and efficient retrieval-augmented generation framework for automated network operations. Our framework has three advantages. The…
Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios
Zhongyuan Wang, Richong Zhang, Zhijie Nie +1
Recent Text-to-SQL methods leverage large language models (LLMs) by incorporating feedback from the database management system. While these methods effectively address execution er…
Improving the Consistency in Cross-Lingual Cross-Modal Retrieval with 1-to-K Contrastive Learning
Zhijie Nie, Richong Zhang, Zhangchi Feng +2
Cross-lingual Cross-modal Retrieval (CCR) is an essential task in web search, which aims to break the barriers between modality and language simultaneously and achieves image-text…
A Text is Worth Several Tokens: Text Embedding from LLMs Secretly Aligns Well with The Key Tokens
Zhijie Nie, Richong Zhang, Zhanyu Wu
Text embeddings from large language models (LLMs) have achieved excellent results in tasks such as information retrieval, semantic textual similarity, etc. In this work, we show an…
Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
Zhangchi Feng, Richong Zhang, Zhijie Nie
The Composed Image Retrieval (CIR) task aims to retrieve target images using a composed query consisting of a reference image and a modified text. Advanced methods often utilize co…
Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval
Hailang Huang, Zhijie Nie, Ziqiao Wang +1
Current image-text retrieval methods have demonstrated impressive performance in recent years. However, they still face two problems: the inter-modal matching missing problem and t…
Towards Better Understanding of Contrastive Sentence Representation Learning: A Unified Paradigm for Gradient
Mingxin Li, Richong Zhang, Zhijie Nie
Sentence Representation Learning (SRL) is a crucial task in Natural Language Processing (NLP), where contrastive Self-Supervised Learning (SSL) is currently a mainstream approach.…
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
Mingxin Li, Richong Zhang, Zhijie Nie +1
Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with the Contrastive Learning of Sentence Embeddings (CSE) being the mainstream t…
Code-Style In-Context Learning for Knowledge-Based Question Answering
Zhijie Nie, Richong Zhang, Zhongyuan Wang +1
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical application…