# LLMs are Also Effective Embedding Models: An In-depth Overview

arXiv: https://arxiv.org/abs/2412.12591 · HTML: https://mlsanity.com/paper/2412.12591/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 163 of its 211 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 30 of 163)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)

## Built on by (most cited first, 30 of 32)

- [Let LLMs Speak Embedding Languages: Generative Text Embeddings via Iterative Contrastive Refinement](https://mlsanity.com/paper/2509.24291/) (2025)
- [SemCoT: Accelerating Chain-of-Thought Reasoning through Semantically-Aligned Implicit Tokens](https://mlsanity.com/paper/2510.24940/) (2025)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [CogDual: Enhancing Dual Cognition of LLMs via Reinforcement Learning with Implicit Rule-Based Rewards](https://mlsanity.com/paper/2507.17147/) (2025)
- [TURA: Tool-Augmented Unified Retrieval Agent for AI Search](https://mlsanity.com/paper/2508.04604/) (2025)
- [Learning Refined Document Representations for Dense Retrieval via Deliberate Thinking](https://mlsanity.com/paper/2502.12974/) (2025)
- [From Topology to Retrieval: Decoding Embedding Spaces with Unified Signatures](https://mlsanity.com/paper/2511.22150/) (2025)
- [ReViP: Mitigating False Completion in Vision-Language-Action Models with Vision-Proprioception Rebalance](https://mlsanity.com/paper/2601.16667/) (2026)
- [The Embedder's Dilemma: LLMs Are Better, but at What Cost?](https://mlsanity.com/paper/2608.12875/) (2026)
- [CoRet: Improved Retriever for Code Editing](https://mlsanity.com/paper/2505.24715/) (2025)
- [A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges](https://mlsanity.com/paper/2507.21117/) (2025)
- [Prompted Policy Search: Reinforcement Learning through Linguistic and Numerical Reasoning in LLMs](https://mlsanity.com/paper/2511.21928/) (2025)
- [MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining](https://mlsanity.com/paper/2604.24374/) (2026)
- [CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement](https://mlsanity.com/paper/2503.17279/) (2025)
- [No Free Lunch in Active Learning: LLM Embedding Quality Dictates Query Strategy Success](https://mlsanity.com/paper/2506.01992/) (2025)
- [The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGENIE from its Bottle](https://mlsanity.com/paper/2603.28643/) (2026)
- [Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes](https://mlsanity.com/paper/2608.02415/) (2026)
- [Research on Violent Text Detection System Based on BERT-fasttext Model](https://mlsanity.com/paper/2412.16455/) (2024)
- [Layer-Aware Embedding Fusion for LLMs in Text Classifications](https://mlsanity.com/paper/2504.05764/) (2025)
- [Enhancing Math Learning in an LMS Using AI-Driven Question Recommendations](https://mlsanity.com/paper/2504.14098/) (2025)
- [LLM-BT-Terms: Back-Translation as a Framework for Terminology Standardization and Dynamic Semantic Embedding](https://mlsanity.com/paper/2506.08174/) (2025)
- [BEACON: Behavioral Malware Classification with Large Language Model Embeddings and Deep Learning](https://mlsanity.com/paper/2509.14519/) (2025)
- [What We Don't C: Manifold Disentanglement for Structured Discovery](https://mlsanity.com/paper/2511.09433/) (2025)
- [VLM2Rec: Resolving Modality Collapse in Vision-Language Model Embedders for Multimodal Sequential Recommendation](https://mlsanity.com/paper/2603.17450/) (2026)
- [TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications](https://mlsanity.com/paper/2604.17778/) (2026)
- [SimReg: Achieving Higher Performance in the Pretraining via Embedding Similarity Regularization](https://mlsanity.com/paper/2605.08809/) (2026)
- [DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark](https://mlsanity.com/paper/2605.30027/) (2026)
- [BitNet Text Embeddings](https://mlsanity.com/paper/2606.25674/) (2026)
- [CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising](https://mlsanity.com/paper/2607.28236/) (2026)
- [RAGas: Retrieval-Augmented Gas Optimization for Smart Contracts with Continuous Knowledge Integration](https://mlsanity.com/paper/2608.15857/) (2026)

## Abstract (arXiv)

Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs such as GPT, LLaMA, and Mistral. This survey provides an in-depth overview of this transition, beginning with foundational techniques before the LLM era, followed by LLM-based embedding models through two main strategies to derive embeddings from LLMs. 1) Direct prompting: We mainly discuss the prompt designs and the underlying rationale for deriving competitive embeddings. 2) Data-centric tuning: We cover extensive aspects that affect tuning an embedding model, including model architecture, training objectives, data constructions, etc. Upon the above, we also cover advanced methods for producing embeddings from longer texts, multilingual, code, cross-modal data, as well as reasoning-aware and other domain-specific scenarios. Furthermore, we discuss factors affecting choices of embedding models, such as performance/efficiency comparisons, dense vs sparse embeddings, pooling strategies, and scaling law. Lastly, the survey highlights the limitations and challenges in adapting LLMs for embeddings, including cross-task embedding quality, trade-offs between efficiency and accuracy, low-resource, long-context, data bias, robustness, etc. This survey serves as a valuable resource for researchers and practitioners by synthesizing current advancements, highlighting key challenges, and offering a comprehensive framework for future work aimed at enhancing the effectiveness and efficiency of LLMs as embedding models.