:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lee, Hyunji, Soldaini, Luca, Cohan, Arman, Seo, Minjoon, Lo, Kyle
Format:	Preprint
Published:	2024
Subjects:	Information Retrieval Artificial Intelligence
Online Access:	https://arxiv.org/abs/2409.02685
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets
by: Weller, Orion, et al.
Published: (2023)

Exploring the Practicality of Generative Retrieval on Dynamic Corpora
by: Kim, Chaeeun, et al.
Published: (2023)

FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions
by: Weller, Orion, et al.
Published: (2024)

Investigating Mixture of Experts in Dense Retrieval
by: Sokli, Effrosyni, et al.
Published: (2024)

GraphRAG-Router: Learning Cost-Efficient Routing over GraphRAGs and LLMs with Reinforcement Learning
by: Fan, Dongzhe, et al.
Published: (2026)

Training Sparse Mixture Of Experts Text Embedding Models
by: Nussbaum, Zach, et al.
Published: (2025)

MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
by: Liu, Lihui, et al.
Published: (2025)

A Self-Supervised Mixture-of-Experts Framework for Multi-behavior Recommendation
by: Kim, Kyungho, et al.
Published: (2025)

ExpertRAG: Efficient RAG with Mixture of Experts -- Optimizing Context Retrieval for Adaptive LLM Responses
by: Gumaan, Esmail
Published: (2025)

Mixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
by: Lin, Xiao, et al.
Published: (2026)

RealRoute: Dynamic Query Routing System via Retrieve-then-Verify Paradigm
by: Liu, Jiahe, et al.
Published: (2026)

LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding
by: Wu, Mingrui, et al.
Published: (2024)

HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation
by: Li, Kunrong, et al.
Published: (2025)

Multi-modal Adaptive Mixture of Experts for Cold-start Recommendation
by: Nguyen, Van-Khang, et al.
Published: (2025)

Hierarchical Time-Aware Mixture of Experts for Multi-Modal Sequential Recommendation
by: Zhang, Shengzhe, et al.
Published: (2025)

Mixture of Experts Approaches in Dense Retrieval Tasks
by: Sokli, Effrosyni, et al.
Published: (2025)

Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases
by: Lei, Yongjia, et al.
Published: (2025)

Better by Comparison: Retrieval-Augmented Contrastive Reasoning for Automatic Prompt Optimization
by: Lee, Juhyeon, et al.
Published: (2025)

GaVaMoE: Gaussian-Variational Gated Mixture of Experts for Explainable Recommendation
by: Tang, Fei, et al.
Published: (2024)

Netflix Artwork Personalization via LLM Post-training
by: Nam, Hyunji, et al.
Published: (2026)

Distillation-based Scenario-Adaptive Mixture-of-Experts for the Matching Stage of Multi-scenario Recommendation
by: Wang, Ruibing, et al.
Published: (2025)

MTmixAtt: Integrating Mixture-of-Experts with Multi-Mix Attention for Large-Scale Recommendation
by: Qi, Xianyang, et al.
Published: (2025)

PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation
by: Khaledian, Arman, et al.
Published: (2025)

Unified Embedding Based Personalized Retrieval in Etsy Search
by: Jha, Rishikesh, et al.
Published: (2023)

MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora
by: Huynh, Tuan-Luc, et al.
Published: (2025)

M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework
by: Zhang, Zijian, et al.
Published: (2024)

Rethinking Hybrid Retrieval: When Small Embeddings and LLM Re-ranking Beat Bigger Models
by: Rao, Arjun, et al.
Published: (2025)

pEBR: A Probabilistic Approach to Embedding Based Retrieval
by: Zhang, Han, et al.
Published: (2024)

HiKEY: Hierarchical Multimodal Retrieval for Open-Domain Document Question Answering
by: Shin, Joongmin, et al.
Published: (2026)

HEAL: Hierarchical Embedding Alignment Loss for Improved Retrieval and Representation Learning
by: Bhattarai, Manish, et al.
Published: (2024)

Efficient Generative Retrieval for E-commerce Search with Semantic Cluster IDs and Expert-Guided RL
by: Zhu, Jianbo, et al.
Published: (2026)

Modernizing Facebook Scoped Search: Keyword and Embedding Hybrid Retrieval with LLM Evaluation
by: Su, Yongye, et al.
Published: (2025)

REMOTE: A Unified Multimodal Relation Extraction Framework with Multilevel Optimal Transport and Mixture-of-Experts
by: Lin, Xinkui, et al.
Published: (2025)

Adaptive Query Routing: A Tier-Based Framework for Hybrid Retrieval Across Financial, Legal, and Medical Documents
by: Hashmi, Afshan
Published: (2026)

mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval
by: Weller, Orion, et al.
Published: (2025)

Advancing AI Research Assistants with Expert-Involved Learning
by: Liu, Tianyu, et al.
Published: (2025)

Dynamic Reasoning Chains through Depth-Specialized Mixture-of-Experts in Transformer Architectures
by: Roy, Sampurna, et al.
Published: (2025)

Multi-Type Context-Aware Conversational Recommender Systems via Mixture-of-Experts
by: Zou, Jie, et al.
Published: (2025)

Reliable Decision Making via Calibration Oriented Retrieval Augmented Generation
by: Jang, Chaeyun, et al.
Published: (2024)

Enhancing the Interpretability of Rule-based Explanations through Information Retrieval
by: Umbrico, Alessandro, et al.
Published: (2025)