Papers with embedding generation
MedEureka: A Medical Domain Benchmark for Multi-Granularity and Multi-Data-Type Embedding-Based Retrieval (2025.findings-naacl)
Copied to clipboard
| Challenge: | Embedding-based retrieval (EBR) is a mainstream approach in information retrieval. |
| Approach: | They propose an enriched benchmark to evaluate retrieval capabilities of embedding models . they use four levels of granularity and six types of medical texts to prompt instruction-fine-tuned embeddable models. |
| Outcome: | The proposed benchmark evaluates the retrieval capabilities of embedding models with multi-granularity and multi-data types. |
Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large language modeling are based on task-related instructions or prompts. |
| Approach: | They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts. |
| Outcome: | The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts . |
Each graph is a new language: Graph Learning with LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Natural language is used to describe graphs, but graph descriptions become verbose and only relying on attribute embeddings limits LLM’s ability to capture adequate graph structural information. |
| Approach: | They propose a graph-defined language for large language model that translates the graph into a corpus instead of graph descriptions and pre-trains LLMs on this corpus to adequately understand the graph. |
| Outcome: | Experiments on five datasets show that the proposed framework outperforms description-based and embedding-based baselines by efficiently modeling different orders of neighbors. |
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been widely explored for embedding generation. |
| Approach: | They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden. |
| Outcome: | The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark. |
Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on recurrent models for text embedding is limited to small task-specific models. |
| Approach: | They propose a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length once it exceeds the vertical chunk size. |
| Outcome: | The proposed architectures achieve competitive performance across benchmarks while maintaining a substantially smaller memory footprint compared to transformer-based models. |