Cover Image

Generalizing Conversational Dense Retrieval via LLM-Cognition Data Augmentation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Haonan, Dou, Zhicheng, Mao, Kelong, Liu, Jiongnan, Zhao, Ziliang
Format:	Preprint
Published:	2024
Subjects:	Computation and Language Information Retrieval
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval
by: Mao, Kelong, et al.
Published: (2024)

CORAL: Benchmarking Multi-turn Conversational Retrieval-Augmentation Generation
by: Cheng, Yiruo, et al.
Published: (2024)

Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation
by: Deng, Chenlong, et al.
Published: (2024)

ProRAG: Process-Supervised Reinforcement Learning for Retrieval-Augmented Generation
by: Wang, Zhao, et al.
Published: (2026)

A Survey of Conversational Search
by: Mo, Fengran, et al.
Published: (2024)

Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding
by: Cheng, Yiruo, et al.
Published: (2024)

History-Aware Conversational Dense Retrieval
by: Mo, Fengran, et al.
Published: (2024)

Chain-of-Retrieval Augmented Generation
by: Wang, Liang, et al.
Published: (2025)

DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation
by: Wang, Shuting, et al.
Published: (2024)

Grounding Language Model with Chunking-Free In-Context Retrieval
by: Qian, Hongjin, et al.
Published: (2024)

Large Language Models for Information Retrieval: A Survey
by: Zhu, Yutao, et al.
Published: (2023)

Query-oriented Data Augmentation for Session Search
by: Chen, Haonan, et al.
Published: (2024)

ConvSDG: Session Data Generation for Conversational Search
by: Mo, Fengran, et al.
Published: (2024)

Little Giants: Synthesizing High-Quality Embedding Data at Scale
by: Chen, Haonan, et al.
Published: (2024)

Metacognitive Retrieval-Augmented Large Language Models
by: Zhou, Yujia, et al.
Published: (2024)

ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval
by: Mo, Fengran, et al.
Published: (2025)

UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations
by: Mo, Fengran, et al.
Published: (2025)

LaSER: Internalizing Explicit Reasoning into Latent Space for Dense Retrieval
by: Jin, Jiajie, et al.
Published: (2026)

FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research
by: Jin, Jiajie, et al.
Published: (2024)

ConvGQR: Generative Query Reformulation for Conversational Search
by: Mo, Fengran, et al.
Published: (2023)

Towards Mixed-Modal Retrieval for Universal Retrieval-Augmented Generation
by: Zhang, Chenghao, et al.
Published: (2025)

Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
by: Zhou, Yujia, et al.
Published: (2024)

Scaling Laws For Dense Retrieval
by: Fang, Yan, et al.
Published: (2024)

Adaptive Retrieval-Augmented Generation for Conversational Systems
by: Wang, Xi, et al.
Published: (2024)

Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models
by: Liu, Wenhan, et al.
Published: (2024)

AugTriever: Unsupervised Dense Retrieval and Domain Adaptation by Scalable Data Augmentation
by: Meng, Rui, et al.
Published: (2022)

Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling
by: Huang, Chao-Wei, et al.
Published: (2024)

CHIQ: Contextual History Enhancement for Improving Query Rewriting in Conversational Search
by: Mo, Fengran, et al.
Published: (2024)

LexRAG: Benchmarking Retrieval-Augmented Generation in Multi-Turn Legal Consultation Conversation
by: Li, Haitao, et al.
Published: (2025)

Graph Retrieval-Augmented LLM for Conversational Recommendation Systems
by: Qiu, Zhangchi, et al.
Published: (2025)

Revela: Dense Retriever Learning via Language Modeling
by: Cai, Fengyu, et al.
Published: (2025)

Optimizing User Profiles via Contextual Bandits for Retrieval-Augmented LLM Personalization
by: Du, Linfeng, et al.
Published: (2026)

Don't Retrieve, Generate: Prompting LLMs for Synthetic Training Data in Dense Retrieval
by: Sinha, Aarush
Published: (2025)

Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search
by: Mo, Fengran, et al.
Published: (2024)

TA-Mem: Tool-Augmented Autonomous Memory Retrieval for LLM in Long-Term Conversational QA
by: Yuan, Mengwei, et al.
Published: (2026)

Preference-based Learning with Retrieval Augmented Generation for Conversational Question Answering
by: Kaiser, Magdalena, et al.
Published: (2025)

AdaCQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment
by: Lai, Yilong, et al.
Published: (2024)

Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
by: Dong, Guanting, et al.
Published: (2024)

Session-level Normalization and Click-through Data Enhancement for Session-based Evaluation
by: Chen, Haonan, et al.
Published: (2024)

DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers
by: Ma, Xueguang, et al.
Published: (2025)