Cover Image

Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Luo, Kun, Qin, Minghao, Liu, Zheng, Xiao, Shitao, Zhao, Jun, Liu, Kang
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models
by: Luo, Kun, et al.
Published: (2024)

Llama2Vec: Unsupervised Adaptation of Large Language Models for Dense Retrieval
by: Liu, Zheng, et al.
Published: (2023)

Flexibly Scaling Large Language Models Contexts Through Extensible Tokenization
by: Shao, Ninglu, et al.
Published: (2024)

InfoFlow: Reinforcing Search Agent Via Reward Density Optimization
by: Luo, Kun, et al.
Published: (2025)

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

VISTA: Visualized Text Embedding For Universal Multi-Modal Retrieval
by: Zhou, Junjie, et al.
Published: (2024)

A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine
by: Xiao, Hanguang, et al.
Published: (2024)

Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies
by: Luo, Xiaoliang, et al.
Published: (2025)

Large Language Models for Planning: A Comprehensive and Systematic Survey
by: Cao, Pengfei, et al.
Published: (2025)

Lighter And Better: Towards Flexible Context Adaptation For Retrieval Augmented Generation
by: Liu, Zheng, et al.
Published: (2024)

Cracking Factual Knowledge: A Comprehensive Analysis of Degenerate Knowledge Neurons in Large Language Models
by: Chen, Yuheng, et al.
Published: (2024)

Extensible Embedding: A Flexible Multipler For LLM's Context Length
by: Shao, Ninglu, et al.
Published: (2024)

Making Large Language Models Efficient Dense Retrievers
by: Lei, Yibin, et al.
Published: (2025)

LLMBox: A Comprehensive Library for Large Language Models
by: Tang, Tianyi, et al.
Published: (2024)

M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation
by: Chen, Jianlv, et al.
Published: (2024)

Exploring Reasoning-Infused Text Embedding with Large Language Models for Zero-Shot Dense Retrieval
by: Liu, Yuxiang, et al.
Published: (2025)

Whispers that Shake Foundations: Analyzing and Mitigating False Premise Hallucinations in Large Language Models
by: Yuan, Hongbang, et al.
Published: (2024)

Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf
by: Xu, Yuzhuang, et al.
Published: (2023)

Making Text Embedders Few-Shot Learners
by: Li, Chaofan, et al.
Published: (2024)

Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
by: Gan, Aoran, et al.
Published: (2025)

Does RAG Really Perform Bad For Long-Context Processing?
by: Luo, Kun, et al.
Published: (2025)

CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph
by: Zhou, Tong, et al.
Published: (2024)

TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling
by: Huang, Cheng, et al.
Published: (2025)

S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models
by: Lei, Fangyu, et al.
Published: (2023)

MegaPairs: Massive Data Synthesis For Universal Multimodal Retrieval
by: Zhou, Junjie, et al.
Published: (2024)

SpecFuse: Ensembling Large Language Models via Next-Segment Prediction
by: Lv, Bo, et al.
Published: (2024)

MoELoRA: Contrastive Learning Guided Mixture of Experts on Parameter-Efficient Fine-Tuning for Large Language Models
by: Luo, Tongxu, et al.
Published: (2024)

Towards Atoms of Large Language Models
by: Hu, Chenhui, et al.
Published: (2025)

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

CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models
by: Lyu, Yuanjie, et al.
Published: (2024)

TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models
by: Chu, Zheng, et al.
Published: (2023)

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

Trade-offs in Large Reasoning Models: An Empirical Analysis of Deliberative and Adaptive Reasoning over Foundational Capabilities
by: Zhao, Weixiang, et al.
Published: (2025)

Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
by: Dang, Yunkai, et al.
Published: (2024)

An Empirical Analysis on Large Language Models in Debate Evaluation
by: Liu, Xinyi, et al.
Published: (2024)

Knowledge in Superposition: Unveiling the Failures of Lifelong Knowledge Editing for Large Language Models
by: Hu, Chenhui, et al.
Published: (2024)

JailBench: A Comprehensive Chinese Security Assessment Benchmark for Large Language Models
by: Liu, Shuyi, et al.
Published: (2025)

Evolving Knowledge Distillation with Large Language Models and Active Learning
by: Liu, Chengyuan, et al.
Published: (2024)

Detecting Anti-Semitic Hate Speech using Transformer-based Large Language Models
by: Liu, Dengyi, et al.
Published: (2024)

Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
by: Xu, Fangzhi, et al.
Published: (2023)