3 citations · 9 across the 14 of their papers we have counts for
16 papers
Douyin Multimodal Embedding Model Technical Report
Haonan Chen, Chu Li, Zhicheng Wang +4
Multimodal representation learning is a cornerstone of modern AI. By encoding multimodal queries and targets into vectors, it powers industrial search and recommendation and underp…
e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
Haonan Chen, Sicheng Gao, Radu Timofte +2
Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that…
Search Self-play: Pushing the Frontier of Agent Capability without Supervision
Hongliang Lu, Yuhang Wen, Pengyu Cheng +7
Reinforcement learning with verifiable rewards (RLVR) has become the mainstream technique for training LLM agents. However, RLVR highly depends on well-crafted task queries and cor…
MoCa: Modality-aware Continual Pre-training Makes Better Bidirectional Multimodal Embeddings
Haonan Chen, Hong Liu, Yuping Luo +4
Multimodal embedding models, built upon causal Vision Language Models (VLMs), have shown promise in various tasks. However, current approaches face three key limitations: the use o…
mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data
Haonan Chen, Liang Wang, Nan Yang +4
Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space.…
Chain-of-Retrieval Augmented Generation
Liang Wang, Haonan Chen, Nan Yang +3
This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer. Conventional R…
Little Giants: Synthesizing High-Quality Embedding Data at Scale
Haonan Chen, Liang Wang, Nan Yang +4
Synthetic data generation has become an increasingly popular way of training models without the need for large, manually labeled datasets. For tasks like text embedding, synthetic…
A Survey of Conversational Search
Fengran Mo, Kelong Mao, Ziliang Zhao +7
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) te…
Query-oriented Data Augmentation for Session Search
Haonan Chen, Zhicheng Dou, Yutao Zhu +1
Modeling contextual information in a search session has drawn more and more attention when understanding complex user intents. Recent methods are all data-driven, i.e., they train…
VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generation
Xuan He, Dongfu Jiang, Ge Zhang +16
The recent years have witnessed great advances in video generation. However, the development of automatic video metrics is lagging significantly behind. None of the existing metric…
LLM experiments with simulation: Large Language Model Multi-Agent System for Simulation Model Parametrization in Digital Twins
Yuchen Xia, Daniel Dittler, Nasser Jazdi +2
This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins…
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
Kelong Mao, Chenlong Deng, Haonan Chen +4
Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization…
Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation
Haonan Chen, Zhicheng Dou, Kelong Mao +2
Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fix…
Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training
Haonan Chen, Zhicheng Dou, Xuetong Hao +3
Cloud solutions have gained significant popularity in the technology industry as they offer a combination of services and tools to tackle specific problems. However, despite their…
Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation
Haonan Chen, Zhicheng Dou, Jiaxin Mao
Since a user usually has to issue a sequence of queries and examine multiple documents to resolve a complex information need in a search session, researchers have paid much attenti…
UniIR: Training and Benchmarking Universal Multimodal Information Retrievers
Cong Wei, Yang Chen, Haonan Chen +5
Existing information retrieval (IR) models often assume a homogeneous format, limiting their applicability to diverse user needs, such as searching for images with text description…