E2Rank (Efficient Embedding-based Ranking) is a unified framework that extends a single text embedding model to perform both retrieval and listwise reranking. By treating the listwise prompt—constructed from the query and its top‑K candidates—as a pseudo‑relevance feedback query, E2Rank reranks via cosine similarity against precomputed document embeddings, avoiding costly autoregressive decoding. The approach achieves state‑of‑the‑art results on BEIR, competitive performance on the reasoning‑intensive BRIGHT benchmark, lower latency than existing LLM‑based rerankers, and improved embedding performance on MTEB—all within a single model.
By Qi Liu, Yanzhao Zhang, Mingxin Li, Dingkun Long, Pengjun Xie, Jiaxin Mao
Should you replace your text-embedding pipeline with a large language model? We answer this with a controlled, cost-aware comparison of ten LLMs across six families and 26 embedding models (118M to 14B parameters) on 37 tasks spanning classification, semantic textual similarity (STS), clustering, pair classification, and retrieval.
arXiv:2609.23307v1 Announce Type: cross
Abstract: This paper presents a comparative evaluation of dense embedding models for semantic candidate-job matching in high-volume staffing workflows. Incomin...
By Sai Yashwant, Siddhartha Jain, Anurag Dubey, Samaroha Chatterjee, Gantala Thulsiram
arXiv:2607. 04071v1 Announce Type: cross Abstract: Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world.
By Lucas Hideki Takeuchi Okamura, Alexandre Alcoforado, Anna Helena Reali Costa
arXiv:2607. 03515v1 Announce Type: cross Abstract: In many machine learning applications, the most relevant items for a query should be efficiently retrieved.
By Kirill Shevkunov, Andrey Ploskonosov, Liudmila Prokhorenkova
The study investigates how document segmentation and chunk representation affect retrieval-augmented generation (RAG) for chemistry texts. Using the ChemQuests corpus, the authors benchmark 41 embedding models and evaluate them across five chunking strategies, seven chunk sizes, and various overlap settings. They find that embedding choice has the largest impact, with models like E5, BGE, and Nomic performing best, and recommend medium-to-large chunks with fixed-token, recursive-token, or hierarchical-section chunking and low overlap for effective chemistry-aware RAG.
By Mahmoud Amiri, Thomas Bocklitz