{"total":156,"rows":[{"id":"2506.05176","title":"Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models","created":"2025-06-05","cited_by_count":1261,"area":null},{"id":"2405.17428","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","created":"2024-05-27","cited_by_count":472,"area":null},{"id":"2404.05961","title":"LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders","created":"2024-04-09","cited_by_count":404,"area":null},{"id":"2407.19669","title":"mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval","created":"2024-07-29","cited_by_count":236,"area":null},{"id":"2407.12883","title":"BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval","created":"2024-07-16","cited_by_count":158,"area":null},{"id":"2403.08540","title":"Language models scale reliably with over-training and on downstream tasks","created":"2024-03-13","cited_by_count":113,"area":null},{"id":"2508.21038","title":"On the Theoretical Limitations of Embedding-Based Retrieval","created":"2025-08-28","cited_by_count":104,"area":null},{"id":"2403.20327","title":"Gecko: Versatile Text Embeddings Distilled from Large Language Models","created":"2024-03-29","cited_by_count":83,"area":null},{"id":"2409.15700","title":"Making Text Embedders Few-Shot Learners","created":"2024-09-24","cited_by_count":79,"area":null},{"id":"2402.15449","title":"Repetition Improves Language Model Embeddings","created":"2024-02-23","cited_by_count":72,"area":null},{"id":"2403.15246","title":"FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions","created":"2024-03-22","cited_by_count":69,"area":null},{"id":"2405.05374","title":"Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models","created":"2024-05-08","cited_by_count":61,"area":null},{"id":"2407.02883","title":"CoIR: A Comprehensive Benchmark for Code Information Retrieval Models","created":"2024-07-03","cited_by_count":60,"area":null},{"id":"2407.00128","title":"When Search Engine Services meet Large Language Models: Visions and Challenges","created":"2024-06-28","cited_by_count":59,"area":null},{"id":"2407.18940","title":"LitSearch: A Retrieval Benchmark for Scientific Literature Search","created":"2024-07-10","cited_by_count":50,"area":null},{"id":"2509.00496","title":"ResearchQA: Evaluating Scholarly Question Answering at Scale Across 75 Fields with Survey-Mined Questions and Rubrics","created":"2025-08-30","cited_by_count":49,"area":null},{"id":"2404.12096","title":"LongEmbed: Extending Embedding Models for Long Context Retrieval","created":"2024-04-18","cited_by_count":48,"area":null},{"id":"2409.11136","title":"Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models","created":"2024-09-17","cited_by_count":40,"area":null},{"id":"2404.18443","title":"BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers","created":"2024-04-29","cited_by_count":38,"area":null},{"id":"2506.20923","title":"KaLM-Embedding-V2: Superior Training Techniques and Data Inspire A Versatile Embedding Model","created":"2025-06-26","cited_by_count":36,"area":null},{"id":"2412.12591","title":"LLMs are Also Effective Embedding Models: An In-depth Overview","created":"2024-12-17","cited_by_count":32,"area":null},{"id":"2505.11293","title":"Breaking the Batch Barrier (B3) of Contrastive Learning via Smart Batch Mining","created":"2025-05-16","cited_by_count":28,"area":null},{"id":"2406.02396","title":"The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding","created":"2024-06-04","cited_by_count":26,"area":null},{"id":"2502.17403","title":"Large Language Models are Powerful Electronic Health Record Encoders","created":"2025-02-24","cited_by_count":26,"area":null},{"id":"2505.15045","title":"Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective","created":"2025-05-21","cited_by_count":23,"area":null},{"id":"2412.03223","title":"Linq-Embed-Mistral Technical Report","created":"2024-12-04","cited_by_count":20,"area":null},{"id":"2412.11919","title":"RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation","created":"2024-12-16","cited_by_count":20,"area":null},{"id":"2502.00409","title":"Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems","created":"2025-02-01","cited_by_count":20,"area":null},{"id":"2510.01003","title":"Improving Code Localization with Repository Memory","created":"2025-10-01","cited_by_count":20,"area":null},{"id":"2404.13556","title":"ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval","created":"2024-04-21","cited_by_count":19,"area":null},{"id":"2406.01607","title":"Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark","created":"2024-05-27","cited_by_count":19,"area":null},{"id":"2408.12194","title":"Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment","created":"2024-08-22","cited_by_count":19,"area":null},{"id":"2502.18460","title":"DRAMA: Diverse Augmentation from Large Language Models to Smaller Dense Retrievers","created":"2025-02-25","cited_by_count":19,"area":null},{"id":"2412.11556","title":"Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs","created":"2024-12-16","cited_by_count":18,"area":null},{"id":"2505.16967","title":"Hard Negatives, Hard Lessons: Revisiting Training Data Quality for Robust Information Retrieval with LLMs","created":"2025-05-22","cited_by_count":17,"area":null},{"id":"2407.20243","title":"Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensions","created":"2024-07-17","cited_by_count":16,"area":null},{"id":"2503.04644","title":"IFIR: A Comprehensive Benchmark for Evaluating Instruction-Following in Expert-Domain Information Retrieval","created":"2025-03-06","cited_by_count":16,"area":null},{"id":"2504.21035","title":"A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage","created":"2025-04-28","cited_by_count":16,"area":null},{"id":"2507.00994","title":"Should We Still Pretrain Encoders with Masked Language Modeling?","created":"2025-07-01","cited_by_count":16,"area":null},{"id":"2410.14635","title":"GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings","created":"2024-10-18","cited_by_count":15,"area":null},{"id":"2504.10471","title":"MIEB: Massive Image Embedding Benchmark","created":"2025-04-14","cited_by_count":15,"area":null},{"id":"2409.05152","title":"OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs","created":"2024-09-08","cited_by_count":14,"area":null},{"id":"2502.11401","title":"Following the Autoregressive Nature of LLM Embeddings via Compression and Alignment","created":"2025-02-17","cited_by_count":14,"area":null},{"id":"2412.14428","title":"WildSAT: Learning Satellite Image Representations from Wildlife Observations","created":"2024-12-19","cited_by_count":13,"area":null},{"id":"2505.12831","title":"Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering","created":"2025-05-19","cited_by_count":13,"area":null},{"id":"2410.10931","title":"Combining Observational Data and Language for Species Range Estimation","created":"2024-10-14","cited_by_count":12,"area":null},{"id":"2411.11927","title":"FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training","created":"2024-11-18","cited_by_count":12,"area":null},{"id":"2504.19099","title":"VeriDebug: A Unified LLM for Verilog Debugging via Contrastive Embedding and Guided Correction","created":"2025-04-27","cited_by_count":12,"area":null},{"id":"2507.07030","title":"UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations","created":"2025-07-09","cited_by_count":12,"area":null},{"id":"2507.19054","title":"Closing the Modality Gap for Mixed Modality Search","created":"2025-07-25","cited_by_count":12,"area":null},{"id":"2603.19223","title":"F2LLM-v2: Inclusive, Performant, and Efficient Embeddings for a Multilingual World","created":"2026-03-19","cited_by_count":12,"area":null},{"id":"2407.07799","title":"Attribute or Abstain: Large Language Models as Long Document Assistants","created":"2024-07-10","cited_by_count":11,"area":null},{"id":"2506.01435","title":"Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings","created":"2025-06-02","cited_by_count":11,"area":null},{"id":"2406.17911","title":"X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation","created":"2024-06-25","cited_by_count":10,"area":null},{"id":"2411.01192","title":"Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks","created":"2024-11-02","cited_by_count":10,"area":null},{"id":"2505.21815","title":"Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking","created":"2025-05-27","cited_by_count":10,"area":null},{"id":"2506.08354","title":"Position: Text Embeddings Should Capture Implicit Semantics, Not Just Surface Meaning","created":"2025-06-10","cited_by_count":10,"area":null},{"id":"2506.15697","title":"DeepRTL2: A Versatile Model for RTL-Related Tasks","created":"2025-05-28","cited_by_count":10,"area":null},{"id":"2509.24291","title":"Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement","created":"2025-09-29","cited_by_count":10,"area":null},{"id":"2410.24200","title":"Length-Induced Embedding Collapse in PLM-based Models","created":"2024-10-31","cited_by_count":9,"area":null},{"id":"2505.21439","title":"Towards Better Instruction Following Retrieval Models","created":"2025-05-27","cited_by_count":9,"area":null},{"id":"2602.11151","title":"Diffusion-Pretrained Dense and Contextual Embeddings","created":"2026-02-11","cited_by_count":9,"area":null},{"id":"2407.09502","title":"From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2502.12732","title":"Circuit Representation Learning with Masked Gate Modeling and Verilog-AIG Alignment","created":"2025-02-18","cited_by_count":8,"area":null},{"id":"2506.04344","title":"GEM: Empowering LLM for both Embedding Generation and Language Understanding","created":"2025-06-04","cited_by_count":8,"area":null},{"id":"2510.02294","title":"F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data","created":"2025-10-02","cited_by_count":8,"area":null},{"id":"2405.19333","title":"Multi-Modal Generative Embedding Model","created":"2024-05-29","cited_by_count":7,"area":null},{"id":"2501.19264","title":"mFollowIR: a Multilingual Benchmark for Instruction Following in Retrieval","created":"2025-01-31","cited_by_count":7,"area":null},{"id":"2505.24754","title":"Don't Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation","created":"2025-05-30","cited_by_count":7,"area":null},{"id":"2408.03402","title":"ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning","created":"2024-08-06","cited_by_count":6,"area":null},{"id":"2410.14578","title":"Large Language Models Are Overparameterized Text Encoders","created":"2024-10-18","cited_by_count":6,"area":null},{"id":"2501.09749","title":"Enhancing Lexicon-Based Text Embeddings with Large Language Models","created":"2025-01-16","cited_by_count":6,"area":null},{"id":"2504.03101","title":"Single-Pass Document Scanning for Question Answering","created":"2025-04-04","cited_by_count":6,"area":null},{"id":"2505.15585","title":"MIRB: Mathematical Information Retrieval Benchmark","created":"2025-05-21","cited_by_count":6,"area":null},{"id":"2509.03020","title":"Training LLMs to be Better Text Embedders through Bidirectional Reconstruction","created":"2025-09-03","cited_by_count":6,"area":null},{"id":"2601.21257","title":"MoCo: A One-Stop Shop for Model Collaboration Research","created":"2026-01-29","cited_by_count":6,"area":null},{"id":"2602.05735","title":"CSRv2: Unlocking Ultra-Sparse Embeddings","created":"2026-02-05","cited_by_count":6,"area":null},{"id":"2406.09790","title":"Pcc-tuning: Breaking the Contrastive Learning Ceiling in Semantic Textual Similarity","created":"2024-06-14","cited_by_count":5,"area":null},{"id":"2410.04981","title":"On the Rigour of Scientific Writing: Criteria, Analysis, and Insights","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2410.20163","title":"UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers","created":"2024-10-26","cited_by_count":5,"area":null},{"id":"2505.15877","title":"Highlighting What Matters: Promptable Embeddings for Attribute-Focused Image Retrieval","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2508.05657","title":"Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation","created":"2025-07-30","cited_by_count":5,"area":null},{"id":"2411.18831","title":"Measuring Risk of Bias in Biomedical Reports: The RoBBR Benchmark","created":"2024-11-28","cited_by_count":4,"area":null},{"id":"2506.16444","title":"REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing","created":"2025-06-19","cited_by_count":4,"area":null},{"id":"2510.22733","title":"E2Rank: Unifying Text Embedding and Listwise Reranking for Effective and Efficient Search","created":"2025-10-26","cited_by_count":4,"area":null},{"id":"2601.12193","title":"VeRVE: Versatile Retrieval for Videos via Unified Embeddings","created":"2026-01-17","cited_by_count":4,"area":null},{"id":"2502.14977","title":"Feedforward Few-shot Species Range Estimation","created":"2025-02-20","cited_by_count":3,"area":null},{"id":"2503.22989","title":"FindTheFlaws: Annotated Errors for Detecting Flawed Reasoning and Scalable Oversight Research","created":"2025-03-29","cited_by_count":3,"area":null},{"id":"2505.11754","title":"Masking in Multi-hop QA: An Analysis of How Language Models Perform with Context Permutation","created":"2025-05-16","cited_by_count":3,"area":null},{"id":"2505.15353","title":"Establishing a Scale for Kullback-Leibler Divergence in Language Models Across Various Settings","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2506.21601","title":"Hierarchical Patch Compression for ColPali: Efficient Multi-Vector Document Retrieval with Dynamic Pruning and Quantization","created":"2025-06-19","cited_by_count":3,"area":null},{"id":"2507.20783","title":"On The Role of Pretrained Language Models in General-Purpose Text Embeddings: A Survey","created":"2025-07-28","cited_by_count":3,"area":null},{"id":"2508.21632","title":"QZhou-Embedding Technical Report","created":"2025-08-29","cited_by_count":3,"area":null},{"id":"2511.14868","title":"Hierarchical Token Prepending: Enhancing Information Flow in Decoder-based LLM Embeddings","created":"2025-11-18","cited_by_count":3,"area":null},{"id":"2601.04350","title":"RIGOURATE: Quantifying Scientific Exaggeration with Evidence-Aligned Claim Evaluation","created":"2026-01-07","cited_by_count":3,"area":null},{"id":"2601.13969","title":"Autonomous Knowledge Graph Exploration with Adaptive Breadth-Depth Retrieval","created":"2026-01-20","cited_by_count":3,"area":null},{"id":"2602.01572","title":"LLM-based Embeddings: Attention Values Encode Sentence Semantics Better Than Hidden States","created":"2026-02-02","cited_by_count":3,"area":null},{"id":"2605.09969","title":"The Truth Lies Somewhere in the Middle (of the Generated Tokens)","created":"2026-05-11","cited_by_count":3,"area":null},{"id":"2409.05199","title":"Interactive Machine Teaching by Labeling Rules and Instances","created":"2024-09-08","cited_by_count":2,"area":null},{"id":"2503.07519","title":"GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval","created":"2025-03-10","cited_by_count":2,"area":null}],"next_cursor":"100"}