Papers with Embedding-based retrieval
MedEureka: A Medical Domain Benchmark for Multi-Granularity and Multi-Data-Type Embedding-Based Retrieval (2025.findings-naacl)
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| 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. |
pEBR: A Probabilistic Approach to Embedding Based Retrieval (2025.emnlp-industry)
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| Challenge: | Existing embedding-based retrieval systems rely on heuristic and suboptimal cutoffs for item retrieval. |
| Approach: | They propose a probabilistic Embedding-Based Retrieval framework that learns a shared semantic representation space for both queries and items. |
| Outcome: | The proposed framework improves retrieval precision and recall, and ablation studies show it captures the differences between head-to-tail queries. |