Vanya Rybkin

I just published Vol. 80 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Comparing Commercial and Open-Source LLMs for Systematic Review Query Generation, from TU Wien 📚 A Systematic Comparison of Ranking Algorithms for LLM Evaluation, from the University of Michigan 📚 A Comprehensive Analysis of LLM Representations for Regression Tasks, from Google 📚 Benchmarking Multi-modal Retrieval-Augmented Generation for Web Content, from Ma et al. 📚 Efficient Large Language Model Scaling for Industrial Dense Retrieval, from Huang et al. 📚 A Survey on LLM-as-a-Judge, from Gu et al. 📚 Reproducing and Adapting 2D Matryoshka Training for Information Retrieval, from Wang et al. 📚 A Fast and Exact Similarity Search Engine for Billion-Scale Data Series, from Schafer et al. 📚 A Review of Visual Question Answering in the Era of Large Language Models, from SYSU 📚 Integrating ID Embeddings and Language Models for Enhanced Sequential Recommendations, from Huawei #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 61 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 GPU-Powered Retrieval System for Billion-Scale Recommendations, from LinkedIn 📚 Enhancing LLM Question Answering through Intent-Driven Knowledge Retrieval, from ASU 📚 A Comprehensive Survey of Recommender Systems from 2017 to 2024, from Raza et al. 📚 A Comprehensive Survey of Retrieval-Augmented Generation Methods for NLP, from Wu et al. 📚 Personalizing Retrieval-Augmented Generation with User-Centric Agents, from The University of Passau 📚 A Systematic Review of LLM Integration in Recommender Systems, from Lin et al. 📚 Efficient Context Compression for Faster and Better Retrieval-Augmented Generation, from Rau et al. 📚 Joint Fine-Tuning and Prompt Optimization for Improved Performance in Complex NLP Systems, from Standford University 📚 Text-Only Approach to Universal Multimodal Embeddings, from Jiang et al. 📚 How Massive Datastores Enhance Language Model Performance, from Shao et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 48 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Systematic Survey of Retrieval-Augmented Generation Techniques, from York University 📚 Navigating the Evaluation Landscape of Generative Information Retrieval, from Alaofi et al. 📚 Leveraging RAG to Enhance the Trustworthiness of Generative AI for Enterprise Workflow Automation, from ServiceNow 📚 Exploring the Interplay Between Large Language Model Priors and Externally Sourced Content in Retrieval-Augmented Systems, from Stanford University 📚 A Practical Roadmap for Efficient Fine-Tuning on Proprietary Enterprise Data, from HCLTech 📚 Leveraging Look-Alike Users and Recalled Items to Improve Click-Through Rate Prediction, from Huawei 📚 Leveraging Collaborative Knowledge in Large Language Models for Robust and Versatile Recommendation, from KAIST 📚 A Study on How LLM-Generated Content Infiltrates and Reshapes Retrieval-Augmented Generation Systems, from UCAS 📚 Leveraging Rotary Position Embeddings for Extended Context Windows in Text Embedding Models, from Microsoft 📚 Taxonomy and Analysis of Sensitive User Queries in Generative AI Search, from Naver #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 54 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Graph Retrieval-Augmented Generation for Context-Aware Reasoning, from Emory University 📚 Enabling Fine-Grained Ranking at Varying Granularities via Multi-Vector Embeddings, from Reddy et al. 📚 Learning to Ask Clarification Questions for Ambiguous Retrieval Queries, from UC Berkeley 📚 Principled and Practical Multi-Vector Retrieval with Approximation Guarantees, from Google Research 📚 Towards User-Centric Recommendations via LLM-Based Simulation and Tool Learning, from USTC 📚 Building Pseudo-Graph Databases from Raw Texts for Retrieval-Augmented Language Models, from Renmin University 📚 Retrieval-Augmented Early Exiting for Efficient Large Language Model Inference, from Huang et al. 📚 A State-of-the-Art Generalist Embedding Model, from NVIDIA 📚 Enhancing Multilingual Language Model Training via Repeat Ranking, from Devine et al. 📚 A Real-World Evaluation of HNSW for Vector Retrieval, from Marqo #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 55 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Comprehensive Review of State-of-the-Art Universal Text Embeddings, from Cao et al. 📚 A Survey of Generative Retrieval Techniques, from NTU 📚 An Approach to Structurally Aligning Visual and Textual Embeddings, from Alibaba 📚 Leveraging Dynamic Granularity for Improved Retrieval-Augmented Generation, from Zhong et al. 📚 A Framework for Consistent and Reliable LLM-based Passage Ranking, from OSU 📚 Boosting Sample Efficiency in Recommender Systems through Large Language Model Integration, from Huawei 📚 A Task-Specific Loss Function for Improved Multi-Task Learning Performance in Online Advertising, from Huawei 📚 Robust Interaction-Based Semantic Relevance Calculation for Large-Scale E-commerce Search Engines, from Alibaba 📚 Comparing Auto-Encoding and Auto-Regression Models in Sequential Recommendation, from Wang et al. 📚 An Unsupervised Pipeline for Measuring Retrieval Complexity and Classifying Difficult Questions in QA, from Amazon Alexa AI #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 56 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Leveraging Large Language Models for Synthetic Query Generation in Virtual Assistants, from Apple 📚 Matryoshka Representations for Enhanced Recommender Systems, from HKBU 📚 Towards Cost-Aware and Accurate Query Retrieval via Progressive LLM-Assisted Expansion, from UCR 📚 In-context Learning vs. Fine-tuning: Evaluating LLM Adaptation for Diverse Dialogue Tasks, from UNITN 📚 A Unified Framework for Text-to-Image Generation and Retrieval with Multimodal Large Language Models, from Qu et al. 📚 Asynchronous Learning of User Representations for Large-Scale Recommendation, from Meta 📚 A Comprehensive Survey for Bridging Generative AI and Industrial Recommenders, from Xu et al. 📚 Leveraging Multi-Head Attention for Improved Retrieval Augmented Generation, from Besta et al. 📚 Rectifying Unsuitable Recommendations without Retraining, from Lai et al. 📚 An Efficient MLP-like Architecture for Sequential Recommendation, from Jian et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 62 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Optimizing Performance and Cost in Long-Context LLM Applications, from Google DeepMind 📚 Bridging the Gap in Long-Context Understanding and RAG Between Open-Access and Proprietary LLMs, from NVIDIA 📚 Beyond Dot Products: Efficient Retrieval with Learned Similarities, from Ding et al. 📚 An Unsupervised Approach to Measuring Retrieval Complexity in Question Answering, from Amazon 📚 A Competitive Mixture-of-Experts Approach to Information Retrieval, from UCAS 📚 Leveraging Web Results and GPT-4 for Improved Sponsored Search, from Microsoft 📚 Ultra-Long User Behavior Modeling in Recommendation Systems, from Kuaishou 📚 Personalizing LLM Prompts for Enhanced Recommendations, from USTC 📚 Enhancing Digital Health Chatbots with Query-Based Retrieval Augmented Generation, from Verily 📚 Mitigating Polarization in Dynamic Recommender Ecosystems, from TikTok #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 64 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Personalized Low-Rank Adaptation of LLMs for Scalable Recommendation Systems, from Zhu et al. 📚 Enhancing RAG for Industrial Knowledge Bases through Reflection-Based Question Augmentation, from An et al. 📚 Reasoning with Large Language Models for Personalized Recommendations, from Google 📚 A Framework for Domain-Specific Evaluation of Retrieval-Augmented Generation Systems, from Zhu et al. 📚 Enhancing LLM Robustness in Noisy Retrieval-Augmented Generation, from Kim et al. 📚 Memory-Efficient Cross-Entropy Approximation for Large-Scale Sequential Recommenders, from Gusak et al. 📚 A Comprehensive Survey of Embedding Compression Techniques in Recommender Systems, from Li et al. 📚 A Year-Long Study on Query Understanding's Impact at Amazon Product Search, from Amazon 📚 Standardizing Sequential Recommender Systems for Reproducible Research, from Betello et al. 📚 A User-Centric Approach to Balancing Relevance and Diversity in Recommender Systems, from Coppolillo et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 75 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Efficient Synthetic Data Generation for Text Embeddings Using Small Language Models, from Chen et al. 📚 Improving Search Relevance Using Large Language Models, from Pinterest 📚 A Technical Survey of Modern Conversational Search Systems, from Mo et al. 📚 A Comprehensive Analysis of Negative Sampling Methods in Large-Scale Recommendation Systems, from Apple 📚 Optimizing E-commerce Search Results Through User Intent Centrality, from Saadany et al. 📚 Understanding the Benefits of Joint Training in LLM-Based Search and Recommendation Systems, from Spotify 📚 A Simple Training-free Approach for LLM-based Recommendations, from Google DeepMind 📚 Optimizing Multi-Channel Fusion for Large-Scale Recommender Systems, from SJTU 📚 Enhancing Long-Context QA through LongRAG, from Zhao et al. 📚 Enhancing E-commerce Content Curation through Automated High Consideration Query Ranking, from Amazon #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 76 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Framework for Evaluating LLM-Based Answer Engines, from Salesforce AI Research 📚 A Comprehensive Survey of LLM-Based Recommender Systems Through an Industrial Lens, from Wang et al. 📚 A Novel Metric for Evaluating Search Result Relevance, from LinkedIn 📚 Enhancing Retrieval Models with In-Context Examples, from Tejaswi et al. 📚 Structured Decomposition for Better Retrieval Augmented Generation, from Verma et al. 📚 Enhancing Retrieval-Augmented Generation Through Contrastive Explanations, from Ranaldi et al. 📚 A Reinforcement Learning Framework for Improving LLM Retrieval, from Hsu et al. 📚 Rule-Guided Retrieval-Augmented Generation for Knowledge-Intensive QA, from Chen et al. 📚 Efficient Collaborative Filtering via Embedding Table Analysis, from Loveland et al. 📚 A Probabilistic Framework for Embedding-Based Retrieval, from Zhang et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 78 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Systematic Review of Language Model Representations, from Zhang et al. 📚 Adaptive RAG Through Knowledge Boundary Detection, from Alibaba 📚 Understanding Context Sufficiency in RAG Systems, from Joren et al. 📚 A Cache-Driven Approach to Scaling Recommendation Systems, from Kuaishou 📚 A General Framework for Content Annotation and Retrieval, from Clarke et al. 📚 A Comprehensive Framework for Multimodal Long Document Understanding, from Alibaba 📚 Specialized Text Embeddings for Financial Document Understanding, from Balyasny Asset Management 📚 A Theoretical Framework for Comparing Recommendation Loss Functions, from Teodoro et al. 📚 Understanding Document Retrieval Trade-offs in RAG Systems, from Intel Labs 📚 A Systematic Evaluation of Commercial LLM Fine-tuning Services, from Stanford University #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 86 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Comprehensive Study of Long Context and RAG Performance, from Li et al. 📚 Search-o1: Agentic Search-Enhanced Large Reasoning Models, from Renmin University 📚 Scalable Multi-task Adaptation of Retrievers for Enterprise Tasks, from ServiceNow 📚 Accelerating Retrieval-Augmented Generation through Pipeline Parallelism, from Amazon 📚 A Semantic Uncertainty Approach to Adaptive Knowledge Integration, from Korea University 📚 A Systematic Review of Cold-Start Recommendation Systems in the LLM Era, from Zhang et al. 📚 Efficient Click-Through Rate Prediction with Graph-Enhanced Retrieval and Adaptive Early Exit LLMs, from Meta 📚 A Comprehensive Survey of The Evolution of Vision Language Models from 2019-2024, from UMD 📚 A Resource-Efficient Framework for Personalizing Large Language Models, from Google DeepMind 📚 Fine-Grained Document Retrieval through Generation-Augmented Learning, from Microsoft #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 81 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Vision for Information Retrieval Systems in the GenAI Era, from CCC Workshop 📚 A Hybrid Framework for Unifying Dense and Generative Retrieval in Sequential Recommendation, from Meta AI 📚 A Framework for Retrieval-Enhanced Medical and Commonsense Reasoning in Language Models, from UMass 📚 An Investigation into Large-Scale Recommendation Models and Their Scaling Behavior, from Guo et al. 📚 A Taxonomy and Generation Framework for Evaluating Retrieval Augmented Generation Systems, from IBM 📚 Practical Constraints in Implementing Feature Interactions for Large-Scale Recommender Systems, from Pinterest 📚 A Self-Optimizing Iterative Approach for Retrieval-Augmented Generation, from UCAS 📚 A Memory-Efficient Alternative to Hierarchical HNSW in High Dimensions, from Munyampirwa et al. 📚 A Study on Retrieval Recall, Document Types, and Prompt Engineering in RAG Systems, from Zhao et al. 📚 Building a Product Knowledge Graph with LLMs for Explainable E-Commerce Recommendations, from eBay #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 60 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Enhancing Retrieval Augmented Generation through Parallel Drafting and Verification, from Google 📚 A Framework for Building Secure and Efficient Enterprise Chatbots with RAG, from NVIDIA 📚 Evaluating RAG vs. Long-Context Models for Environmental Policy Document Comprehension, from Phan et al. 📚 Bridging Language Models and Collaborative Filtering for Enhanced Recommendations, from Sheng et al. 📚 Refining Retrieved Content to Boost RAG Effectiveness, from The University of Lisbon 📚 Optimal Methods for Crowd-Sourcing Realistic Multi-modal Dialogue Data in E-commerce, from Amazon 📚 Improving HNSW for Efficient Dynamic Indexing, from UESTC 📚 Balancing Factual Information and Token Efficiency in Black-Box RAG Systems, from Zhejiang University 📚 Enhancing RAG Systems with Unsupervised Document Refinement, from KAIST 📚 An Approach to Multi-Modal Information Retrieval and LLM Integration, from Zhejiang University #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 65 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction, from Sarmah et al. 📚 A Method for Enhancing Precision in Dense Retrieval for E-commerce Search, from Walmart 📚 Enhancing E-commerce Search with LLM-Powered Attribute Extraction, from Amazon 📚 Enhancing Ad Retrieval with Joint Optimization of Hierarchical Clustering and Neural Networks, from Meta 📚 Integrating Web Search and Knowledge Graphs in Retrieval-Augmented Generation, from Xie et al. 📚 Enhancing BM25 with Entropy-Weighted Similarity and Semantic Understanding, from Li et al. 📚 A Novel GNN Framework for Multimodal and Multilingual Product Recommendations in E-commerce, from Amazon 📚 Human-Guided Optimization of Embedding-Based Retrieval for E-commerce, from Walmart 📚 A Comprehensive Guide to Similarity Measures in Data Science, from Levy et al. 📚 Evaluating Mamba Architecture for Dense Retrieval Tasks, from Renmin University #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 82 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 An Efficient Two-Tower BERT Architecture for Large-Scale Product Search, from Walmart 📚 A Unified Framework for Text Embeddings in the Age of Large Language Models, from Nie et al. 📚 Arctic-Embed 2.0: Breaking the English-Multilingual Performance Trade-off in Text Embeddings, from Snowflake 📚 JINA-CLIP-V2: A Three-Stage Framework for Cross-Modal and Text-Only Retrieval, from Jina AI 📚 A Comprehensive Framework for Evaluating Document Parsing Systems, from Shanghai AI Laboratory 📚 Amortized Inference for User History Modeling in Recommendation Systems, from LinkedIn 📚 Leveraging Natural Language Preferences for Personalized Sequential Recommendations, from Meta 📚 Exploring the Scaling Behavior of Transformer-based Sequential Recommendation Models, from Mercado Libre 📚 Bridging the Gap Between Rerankers and Generators in Retrieval-Augmented Generation via Rationale Distillation, from CityU 📚 Hierarchical Embedding Alignment Loss for Enhanced Retrieval and Representation Learning in RAG Systems, from Bhattarai et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 83 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 ModernBERT: A Next-Generation Encoder for Fast and Efficient Natural Language Processing, from Warner et al. 📚 Token-Based Knowledge Assessment for Optimizing RAG Systems, from Naver 📚 A Systematic Survey of LLM-Enhanced Recommender Systems, from Liu et al. 📚 Disentangling Position Bias in Retrieval-Augmented Generation, from Snowflake AI Research 📚 A Comprehensive Survey of LLM-based Embedding Models, from Tao et al. 📚 A Comparative Study of Retrieval Strategies and Prompt Engineering, from Papadimitriou et al. 📚 Bridging Visual and Textual Understanding in Multi-Document QA, from Adobe 📚 Investigating Mixture of Experts in Dense Retrieval, from UNIMIB 📚 A Comprehensive Analysis of Mamba Models for Text Reranking, from the University of Utah 📚 Improving Passage Ranking Efficiency Through Long-Context LLM Full Ranking, from Liu et al. #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 63 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 A Modular Approach to Retrieval-augmented Generation, from Gao et al. 📚 A Survey on Retrieval Methods in Modern Recommender Systems, from SJTU 📚 A Self-Guided Approach for More Robust RALMs, from Baidu 📚 Efficient Retrieval Planning for Complex Conversational AI Systems, from Amazon 📚 Efficient Long-Context Multilingual Text Retrieval Models, from Alibaba 📚 Integrating Multimodal Representations in E-commerce Recommendations, from Alibaba 📚 A Systematic Review of Recommender System Challenges and Opportunities, from Ma et al. 📚 A Multi-Agent Framework for Advanced Web Information Seeking and Integration, from Chen et al. 📚 Efficient Dimensionality Reduction for LLM Embeddings in Information Retrieval, from Google Cloud AI 📚 Improving Text Embeddings in Resource-Efficient Language Models, from Tsinghua University #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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I just published Vol. 68 of "Top Information Retrieval Papers of the Week" on Substack. My Substack newsletter features the 7-10 most notable research papers on information retrieval (including recommender systems, search & ranking, etc.) from each week, with a brief summary, and links to the paper/codebase. This week’s newsletter highlights the following research work: 📚 Retrieval-Augmented Generation for Superior Long-Context Question Answering, from NVIDIA 📚 A Near Real-Time Ranking Platform for Modern E-Commerce, from Zalando 📚 A Method for Automated Task-Specific Dataset Creation and Model Fine-Tuning, from Ziegler et al. 📚 A Lightweight Framework for Combining Domain Experts in Information Retrieval, from Lee et al. 📚 Dynamically Determining Compression Rates for Improved RAG Performance, from Zhang et al. 📚 A Comprehensive Study of Pooling and Attention Strategies for Optimizing LLM-Based Embedding Models, from HKUST 📚 Unbiasing Short-Video Recommenders through Causal Inference, from Dong et al. 📚 Non-Parametric Embedding Fine-Tuning for Improved k-NN Retrieval, from Zeighami et al. 📚 A High-Performance Batch Query Architecture for Industrial-Scale Recommendation Systems, from Zhang et al. 📚 Correcting Stale Embeddings for Computationally Efficient Dense Retrieval, from Google DeepMind #InformationRetrieval #ResearchPapers #CuratedContent #Newsletter  #substack

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