Text Embedding Models

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Text embedding models turn language into vectors so systems can compare texts, retrieve relevant documents, group content, or use text as features in other tasks. Common approaches include contextual transformer encoders trained with contrastive learning, hard negatives, synthetic query–document pairs, and distillation; newer methods also adapt large language models through prompting, reasoning objectives, or test-time refinement. Work spans multilingual and domain-specific retrieval, long-context representation, and efficient deployment through compression, quantization, and smaller student models. A central challenge is that benchmark scores can depend heavily on prompts, datasets, and task type, so stronger general-purpose performance does not guarantee robust results in a particular language or application.

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126 papers16 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Yalda-Embedding: Open Development of a Competitive Persian Retrieval Model
Oct 23, 2026Yalda Taheri, Mohammad Hassan Heydari, Armon RasooliText Embedding Models Information Retrieval
Faculty of Engineering, Azad University · Faculty of Computer Engineering, University of Isfahan · Department of Electrical Engineering, Iran University of Science and Technology
ULME: Predictive Coupling for Mutually Reinforcing Embedding and Language Modeling
Oct 22, 2026Yang Liu, Zhiqiang Yuan, Shu-Dong Huang +5Text Embeddings Text Embedding Models
Pattern Recognition Center, WeChat AI, Tencent Inc. · College of Computer Science, Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education
Learning When to Think: Utility-Guided Adaptive Reasoning for Efficient Text Retrieval
Oct 15, 2026Yingtao Luo, Kangyi Zhao, Shiyi Du +1Text Embedding Models Efficient Language Model Reasoning
Carnegie Mellon University · University of Pittsburgh
Beyond Black Boxes: Visual Grounding for Text Embeddings
Oct 13, 2026Slimane LarabiInformation Retrieval Text Embedding Models
Computer Science Faculty, USTHB University
Self-improving data synthesis enables scalable adaptation of embedding models
Oct 10, 2026Zucan Lyu, Rui Tang, Jianlyu Chen +2Synthetic Data Generation Information Retrieval
University of Science and Technology of China, Hefei, China. · State Key Laboratory of Cognitive Intelligence, Hefei, China.
Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems
Oct 10, 2026Ekta YadavText Embedding Models Recommender Systems
Randstad N.V.
SciTBERT: A family of chronologically consistent language models for scientific and technological language processing
Oct 8, 2026Thomas Gebhart, Russell J. FunkLanguage Modeling Text Embedding Models
Carlson School of Management, University of Minnesota
MetaEncoder: Exploring the Limit of Bi-Encoders for Multimodal System One Decision Making with Natural Language Interface
Oct 8, 2026Jianpeng Cheng, Guangyu Sun, Aashu Singh +11Text Embedding Models Cross-Modal Retrieval
Meta AI
Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models
Oct 7, 2026Amanda Myntti, Jenna Kanerva, Veronika Laippala +1Text Embedding Models Information Retrieval
TurkuNLP University of Turku, Finland · Ellis Institute Finland
Learning to Retrieve via Reinforcement Learning in Embedding Space
Oct 6, 2026Qi Liu, Fengming Liang, Yiqun Chen +2RL for IR Text Embedding Models
Renmin University of China
Lend Me Your Eyes: Instruction-Aware Text Embeddings via Attention Relay
Oct 4, 2026Yiyuan Luo, Vaggos ChatziafratisText Embeddings Attention Mechanisms
University of California, Santa Cruz
Building Interpretable Feature Representations for Resume-Vacancy Matching by Distilling Production LLM Signals
Oct 2, 2026Ilya Chekin, Vyacheslav Malyugin, Vladimir Chirkov +1Job-Candidate Matching Interpretable ML
BroutonLab · Curately
From Retrieval to Typed Decisions: Calibrated System One Models from Biomedical Sentence Encoders
Oct 1, 2026Pritam DekaText Classification Probability Calibration
Queen’s University Belfast Belfast, United Kingdom
Scaling and Distilling Text Embeddings for Better Diffusibility
Oct 1, 2026Zekai Zhang, Yunjie Tian, Yanjin He +4Word Embeddings Language Model Distillation
University of Michigan
Effective Dense Retrieval using Only In-Context Examples
Sep 29, 2026Nour Jedidi, Abdul Basit Ali, Hang Li +1LLM Prompting Text Embedding Models
University of Waterloo · The University of Queensland
Selecting What Matters: Semantic Compression-Guided Selective Pooling for Long-Context Embeddings
Sep 29, 2026Zifeng Cheng, Jie Zheng, Zhiwei Jiang +4Text Embeddings Text Embedding Models
State Key Laboratory for Novel Software Technology, Nanjing University · National University of Singapore · Nanjing University of Posts and Telecommunications
Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs
Sep 26, 2026Zifeng Cheng, Lingyun Qian, Zhiwei Jiang +5Text Embeddings Text Embedding Models
State Key Laboratory for Novel Software Technology, Nanjing University · National University of Singapore
Semantic Candidate-Job Matching: A Comparative Evaluation of Dense Embedding Models in Hybrid Retrieval
Sep 20, 2026Sai Yashwant, Siddhartha Jain, Anurag Dubey +2Job-Candidate Matching Text Embedding Models
ManpowerGroup Services India Pvt. Ltd. · Indian Institute of Technology, Hyderabad
Embedding Models Measure in Peculiar Ways
Sep 17, 2026Juri Opitz, Andrianos MichailText Embeddings Text Embedding Evaluation
Dept. of Computational Linguistics University of Zurich
Where Post-Training Quantization Breaks Text Embedders: A Measured Map Across Four Embedder Families
Sep 14, 2026Hyojung HanText Embedding Evaluation Weight-Only Quantization
ThakiCloud
FastE: Readout-Triggered Token Compression for LLM Embedding Inference
Sep 8, 2026Jinsong Shu, Jinyong Wen, Baokun Wang +4Token Pruning LLM Inference Acceleration
Zhejiang University · Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security · Ant Group +1
Recovering Temporal and Geographic Signals from Language Model Embeddings
Sep 4, 2026Esteban Feuerstein, Victoria Klimkowski, Juan Manuel Ortiz de Zarate +1Text Embedding Models LLM Interpretability
Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires · Instituto de Ciencias de la Computación, CONICET-UBA
Do General NLP Embeddings Capture Ontological Reasoning?
Aug 31, 2026Hamed Babaei Giglou, Jennifer D'Souza, Sören AuerLogical Reasoning Text Embedding Evaluation
TIB Leibniz Information Centre for Science and Technology · Hannover, Lower Saxony, Germany · L3S Research Center, Leibniz University of Hannover
Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster
Aug 31, 2026Songtao Fang, Zihao Xu, Shaowei Wei +2Generative Retrieval Information Retrieval
Alibaba Group HangZhou, CHINA
REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling
Aug 30, 2026Devrim Çavuşoğlu, Emre AkbaşLong-Context Retrieval Long-Context Modeling
Middle East Technical University · OBSS AI
From Location Phrases to Geographic Entities: Task-Adapted Retrieval for People Search
Aug 29, 2026Yanbo Li, Chujie Zheng, Jiahao Xu +8Information Retrieval Text Embedding Models
LinkedIn Sunnyvale, CA, USA
Embedding Models for Stance-Aware Argument Retrieval
Aug 28, 2026Angelo Sparacino, Francesca Toni, Adam DejlComputational Argumentation Text Embedding Models
Department of Computing, Imperial College London, UK
MoganBert-TR: A Turkish Encoder Foundation Model Trained from Scratch with a CLM-to-MLM Curriculum
Aug 26, 2026Furkan Yilmaz, Habibe Aleyna Tasdemir, Muhammed Faruk GozayLanguage Model Pretraining Language Modeling
Giga-Embeddings: Mixture-of-Experts Encoders for High-Throughput Text Embeddings
Aug 24, 2026Egor Kolodin, Egor Krasnoperov, Evgeniy Kosarev +1Text Embedding Models Mixture-of-Experts Models
1MIPT · 2SaluteDevices · 3MSU
GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval
Aug 23, 2026Surya SakaText Embedding Models Legal IR
JudicialMind

Oct 23, 2026J/K move · Enter open · S save

Yalda-Embedding: Open Development of a Competitive Persian Retrieval Model

Yalda Taheri, Mohammad Hassan Heydari, Armon Rasooli

Faculty of Engineering, Azad University · Faculty of Computer Engineering, University of Isfahan · Department of Electrical Engineering, Iran University of Science and Technology

Text Embedding Models Information Retrieval Dense Retrieval Contrastive Learning Knowledge Distillation

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Despite advances in multilingual text representations, effective dense retrieval for Persian remains constrained by the limited availability of language-specific training resources and compact embedding models. We present Yalda Embedding, a 595.8-million-parameter Persian retrieval model adapted from Qwen3-Embedding-0.6B using more than one million query-document pairs. On eight Persian retrieval tasks from FaMTEB, the model achieves the highest macro-average among the evaluated 0.6B models, scoring 0.5488 and outperforming Qwen3-Embedding-0.6B and F2LLM-v2-0.6B on every task. It also approaches Qwen3-Embedding-4B, winning on four tasks, tying on one, and trailing by 0.0075 in macro-average. An incremental ablation study examines contrastive batch size, optimization stabilization, and relational knowledge distillation from F2LLM-v2-8B. Under the one-epoch training setup, contrastive fine-tuning alone yields a modest mean improvement over initialization and regressions on two tasks. The final distilled configuration improves all eight tasks over the non-distilled model, raising the mean from 0.4813 to 0.5488 without increasing inference-time model size. These gains include hard-negative evaluation tasks despite training without explicitly mined or supplied hard negatives. We release model weights, training and evaluation code, the pairwise dataset, and its teacher-embedded counterpart, enabling inspection of data composition and training procedures, independent evaluation, and reuse without repeating expensive teacher inference.