{"total":41,"rows":[{"id":"2210.07316","title":"MTEB: Massive Text Embedding Benchmark","created":"2022-10-13","cited_by_count":640,"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":"2209.11055","title":"Efficient Few-Shot Learning Without Prompts","created":"2022-09-22","cited_by_count":137,"area":null},{"id":"2409.15700","title":"Making Text Embedders Few-Shot Learners","created":"2024-09-24","cited_by_count":79,"area":null},{"id":"2311.08147","title":"RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge","created":"2023-11-14","cited_by_count":51,"area":null},{"id":"2511.07025","title":"Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Tasks","created":"2025-11-10","cited_by_count":51,"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":"2407.08188","title":"Position: Measure Dataset Diversity, Don't Just Claim It","created":"2024-07-11","cited_by_count":26,"area":null},{"id":"2407.16695","title":"Stress-Testing Long-Context Language Models with Lifelong ICL and Task Haystack","created":"2024-07-23","cited_by_count":19,"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":"2506.01435","title":"Redundancy, Isotropy, and Intrinsic Dimensionality of Prompt-based Text Embeddings","created":"2025-06-02","cited_by_count":11,"area":null},{"id":"2305.12576","title":"Automated Few-shot Classification with Instruction-Finetuned Language Models","created":"2023-05-21","cited_by_count":9,"area":null},{"id":"2508.17744","title":"Randomly Removing 50% of Dimensions in Text Embeddings has Minimal Impact on Retrieval and Classification Tasks","created":"2025-08-25","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":"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":"2501.09749","title":"Enhancing Lexicon-Based Text Embeddings with Large Language Models","created":"2025-01-16","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":"2412.07462","title":"Bilingual BSARD: Extending Statutory Article Retrieval to Dutch","created":"2024-12-10","cited_by_count":5,"area":null},{"id":"2505.13136","title":"New Encoders for German Trained from Scratch: Comparing ModernGBERT with Converted LLM2Vec Models","created":"2025-05-19","cited_by_count":5,"area":null},{"id":"2401.13621","title":"DenoSent: A Denoising Objective for Self-Supervised Sentence Representation Learning","created":"2024-01-24","cited_by_count":4,"area":null},{"id":"2509.12892","title":"Conan-Embedding-v2: Training an LLM from Scratch for Text Embeddings","created":"2025-09-16","cited_by_count":4,"area":null},{"id":"2311.00408","title":"AdaSent: Efficient Domain-Adapted Sentence Embeddings for Few-Shot Classification","created":"2023-11-01","cited_by_count":3,"area":null},{"id":"2506.07438","title":"LGAI-EMBEDDING-Preview Technical Report","created":"2025-06-09","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":"2510.06730","title":"PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs","created":"2025-10-08","cited_by_count":3,"area":null},{"id":"2605.22544","title":"One prompt is not enough: Instruction Sensitivity Undermines Embedding Model Evaluation","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2605.23033","title":"Uncovering the Latent Potential of Deep Intermediate Representations","created":"2026-05-21","cited_by_count":3,"area":null},{"id":"2310.17690","title":"Non-contrastive sentence representations via self-supervision","created":"2023-10-26","cited_by_count":2,"area":null},{"id":"2604.03420","title":"Zero-Shot Quantization via Weight-Space Arithmetic","created":"2026-04-03","cited_by_count":2,"area":null},{"id":"2409.16668","title":"Topic-aware Causal Intervention for Counterfactual Detection","created":"2024-09-25","cited_by_count":1,"area":null},{"id":"2410.00179","title":"Evaluating the fairness of task-adaptive pretraining on unlabeled test data before few-shot text classification","created":"2024-09-30","cited_by_count":1,"area":null},{"id":"2506.02561","title":"Pruning General Large Language Models into Customized Expert Models","created":"2025-06-03","cited_by_count":1,"area":null},{"id":"2605.12714","title":"Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2605.28190","title":"The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness","created":"2026-05-27","cited_by_count":1,"area":null},{"id":"2311.16185","title":"Enhancing Sentiment Analysis Results through Outlier Detection Optimization","created":"2023-11-25","cited_by_count":0,"area":null},{"id":"2401.03563","title":"Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning","created":"2024-01-07","cited_by_count":0,"area":null},{"id":"2605.15081","title":"ML-Embed: Inclusive and Efficient Embeddings for a Multilingual World","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.16608","title":"To MRL or not to MRL: Text Embeddings are Robust to Truncation Without Matryoshka Learning, Except In Heavy Truncation Scenarios","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2606.25674","title":"BitNet Text Embeddings","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2609.24322","title":"The Undetected Damage of Quantization on Retrieval and How to Fix It","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2610.17096","title":"Towards Universal Calibration for In-Context Classification","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}