Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT

By Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha, Christopher Ré. 2024

Subfields: Information Retrieval & Search, Model Architectures, Evaluation & Benchmarks

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In Information Retrieval & Search

1. Luke Merrick

In Evaluation & Benchmarks

1. Luke Merrick

Citations in the corpus

• Arctic-Embed: Scalable, Efficient, and Accurate Text Embedding Models (2024, methodology): “While these LoCo results suggest m-long may not be the model of choice for long sequences, its strong MTEB Retrieval scores suggest it may be a good pick for datasets containing a mix of long and short sequences.”