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Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection

arXiv 2404.00458 ↗First posted 30 Mar 2024cs.CL cs.IRcited by 0

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Abstract (arXiv)

This position paper proposes a systematic approach towards developing a framework to help select the most effective embedding models for natural language processing (NLP) tasks, addressing the challenge posed by the proliferation of both proprietary and open-source encoder models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 10 references that are in the map. First posted on arXiv 30 Mar 2024.

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6 of 10 references.

References · 6

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  4. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,252
  5. 2022GPT-NeoX-20B: An Open-Source Autoregressive Language Modelcited by 713
  6. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361

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