19 citations · 64 across the 25 of their papers we have counts for
10 papers · 1 filter
Enhancing Dense Retrievers' Robustness with Group-level Reweighting
Peixuan Han, Zhenghao Liu, Zhiyuan Liu +1
The anchor-document data derived from web graphs offers a wealth of paired information for training dense retrieval models in an unsupervised manner. However, unsupervised data con…
MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugin
Tianshuo Zhou, Sen Mei, Xinze Li +5
This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval. MARVE…
Toolink: Linking Toolkit Creation and Using through Chain-of-Solving on Open-Source Model
Cheng Qian, Chenyan Xiong, Zhenghao Liu +1
Large Language Models (LLMs) have demonstrated remarkable progress in utilizing tools, but their closed-source nature and high inference costs pose limitations on their adaptabilit…
Improving Multitask Retrieval by Promoting Task Specialization
Wenzheng Zhang, Chenyan Xiong, Karl Stratos +1
In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-spe…
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
Xinze Li, Zhenghao Liu, Chenyan Xiong +4
This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. S…
Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In
Zichun Yu, Chenyan Xiong, Shi Yu +1
Retrieval augmentation can aid language models (LMs) in knowledge-intensive tasks by supplying them with external information. Prior works on retrieval augmentation usually jointly…
Model-Generated Pretraining Signals Improves Zero-Shot Generalization of Text-to-Text Transformers
Linyuan Gong, Chenyan Xiong, Xiaodong Liu +5
This paper explores the effectiveness of model-generated signals in improving zero-shot generalization of text-to-text Transformers such as T5. We study various designs to pretrain…
Unsupervised Dense Retrieval Training with Web Anchors
Yiqing Xie, Xiao Liu, Chenyan Xiong
In this work, we present an unsupervised retrieval method with contrastive learning on web anchors. The anchor text describes the content that is referenced from the linked page. T…
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Shi Yu, Chenghao Fan, Chenyan Xiong +3
Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we prop…
Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories
Suyu Ge, Chenyan Xiong, Corby Rosset +3
In this paper we improve the zero-shot generalization ability of language models via Mixture-Of-Memory Augmentation (MoMA), a mechanism that retrieves augmentation documents from m…