Text Embedding Models

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6 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

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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
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
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
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
There is No Theoretical Curse of Multilinguality For Embedding Space Structure
Aug 17, 2026Niyati Bafna, Neha Verma, Vilém Zouhar +2Multilingual Language Models Cross-Lingual Representation Alignment
Johns Hopkins University, Center for Language and Speech Processing · ETH Zurich
GEM: A Generative Embedding Model Bridging Reasoning and Retrieval
Aug 13, 2026Zhili Shen, Craig MacdonaldText Embedding Models
University of Glasgow
The Embedder's Dilemma: LLMs Are Better, but at What Cost?
Aug 13, 2026Adnan El Assadi, Niklas Muennighoff, Jinhyuk LeeLLM Inference Efficiency LLM Evaluation
Harvard University · Stanford University · Independent Researcher
How Far Do Simple Transformations Translate Across Text Embedding Models?
Aug 6, 2026Sid Ali Hamideche, Louis-Adrien Dufrène, Quentin Lampin +1Representation Learning Text Embedding Evaluation
Orange Research · Orange
Mapping Similarity Spaces across Embedding Models with Synthetic Query Probing
Aug 6, 2026Marcin Rozmus, Peter van der PuttenText Embedding Evaluation Text Embedding Models
GenAI Engineering, Pegasystems, Kraków, Poland · AI Lab, Pegasystems, Amsterdam, The Netherlands · LIACS, Leiden University, Leiden, the Netherlands
Task-Conditional Flow Matching for Balanced Multilingual Text Embedding Adaptation
Aug 6, 2026Tirth Bhatt, Naren Kumar S, Mayank SinghFlow Matching Text Embeddings
LINGO Research Group, Indian Institute of Technology Gandhinagar, India
Bi-semantic Chemical Embedder for Joint Representation Learning of SMILES and Natural Language
Aug 4, 2026David Ming Segura, Jeremy Goumaz, Joshua W. Sin +2Text Embedding Models Molecular Representation Learning
Laboratory of Artificial Chemical Intelligence (LIAC), EPFL, Lausanne, Switzerland · Process Chemistry & Catalysis, Synthetic Molecules Technical Development, F. Hoffmann-La Roche AG, Basel, Switzerland · National Centre of Competence in Research (NCCR) Catalysis, EPFL, Lausanne, Switzerland
Beyond Scale and Generation: Understanding Language Model-based Entity Matching
Jul 27, 2026Zeyu Zhang, Xue Li, Iacer Calixto +2Text Embedding Models
University of Amsterdam & AUMC · CWI · University of Amsterdam +1
Choosing a Text Embedding Model: A Practical Benchmarking and Decision Framework
Jul 26, 2026Madhav S BaidyaModel Selection Text Embedding Evaluation
Indian Institute of Technology (BHU) Varanasi
KeySI: An Interaction Framework for Tuning Text Embeddings Based on Human Feedback
Jul 17, 2026Yan Zhu, Y. Chen, Rebecca FaustText Embeddings Text Embedding Models
Yan Zhu is with Tulane University. · Y. Chen is with Tulane University. · Rebecca Faust* is with Tulane University.
Automated Compliance Mapping in Cloud Security with Domain-Adapted Sentence Transformers
Jul 7, 2026John Bianchi, Luca Petrillo, Fabio Martinelli +1Semantic Textual Similarity Domain Adaptation
Institute for Informatics and Telematics (IIT-CNR), Pisa, Italy · IMT School for Advanced Studies Lucca, Lucca, Italy · Institute for High Performance Computing and Networking (ICAR-CNR), Rende (CS), Italy
MTEB-BR: A Text Embedding Benchmark for Brazilian Portuguese
Jul 6, 2026Tardelli Ronan Coelho StekelText Embedding Evaluation Text Embedding Models
Federal Institute of S˜ao Paulo (IFSP) S˜ao Paulo, Brazil
Separating Representation from Reconstruction Enables Scalable Text Encoders
Jul 4, 2026Megi Dervishi, Mathurin Videau, Yann LeCunMasked Language Modeling Self-Supervised Pre-Training
FAIR · 3New York University
Field Order Should Not Matter: Permutation-Invariant Embedding Model Fine-Tuning for Structured Metadata Retrieval
Jun 29, 2026Aivin V. Solatorio, Olivier Dupriez, Rafael MacalabaMultilingual IR Information Retrieval
Development Data Group, Office of the Chief Statistician, World Bank Group
A Comparative Study on Affective Cues in Text Embeddings Across Psychological Emotion Theories
Jun 27, 2026Fabio Ciani, Harald Schweiger, Emilia Parada-Cabaleiro +1Text Embeddings Text Embedding Evaluation
Department of Electronics, Information, and Bioengineering, Polytechnic University of Milan · Multimedia Mining and Search Group, Institute of Computational Perception, Johannes Kepler University Linz · Department of Music Pedagogy, Nuremberg University of Music +1
BitNet Text Embeddings
Jun 24, 2026Zhen Li, Xin Huang, Liang Wang +8LLM Quantization Embedding Compression
Peking University · Microsoft Research
DREAM: Dense Retrieval Embeddings via Autoregressive Modeling
Jun 23, 2026Yixuan Tang, Yi YangInformation Retrieval Text Embedding Models
The Hong Kong University of Science and Technology
PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models
Jun 23, 2026Lin Lawrence Guo, Adam Paul Yan, Emily Vettese +1Clinical Outcome Prediction Text Embedding Models
Child Health Evaluative Sciences · The Hospital for Sick Children · Toronto, Canada +1
Does My Embedding Reflect That $A = B$ ? Evaluating Mathematical Equivalence in Embedding Models
Jun 22, 2026Jiaying Ye, Samarth Rao, Leo Carlin +9Contrastive Learning Text Embedding Evaluation
University of Washington · Axiom Math, University of Washington · Pacific Northwest National Laboratory

Oct 7, 2026, cs.CLJ/K move · Enter open · S save

Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models

Amanda Myntti, Jenna Kanerva, Veronika Laippala, Filip Ginter

TurkuNLP University of Turku, Finland · Ellis Institute Finland

Text Embedding Models Information Retrieval Text Embedding Evaluation

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Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval prompt. Several new datasets and studies have examined this setting, showing that the current embedding models often struggle to follow such instructions reliably. In this paper, we study the mechanism of how instructions actually affect the representations of retrieval queries in asymmetric retrieval tasks. We show that models can fail to follow even simple task instructions when query-side distractors are included in the evaluation. We hypothesize that this behavior is driven by the training setup of current embedding models and their evaluation, and show that fine-tuning with added query-side distractors leads to substantial improvements, with minimal effect on other tasks.

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