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Status: Unchecked ×Keyword: specters ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   SciRepEval: A Multi-Format Benchmark for Scientific Document Representations

   Singh, D’Arcy, Cohan, Downey and Feldman · Conference on Empirical Methods in Natural Language Processing (EMNLP) · 2023

   The paper introduces SciRepEval, a 24-task benchmark for scientific document representations, uses it to study generalisation, and releases SPECTER2, models that learn multiple embeddings per document.

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   1. UncheckedLeading scientific document models SPECTER and SciNCL struggle to generalise across task formats, and simple multi-task training does not improve them.“We show how state-of-the-art models like SPECTER and SciNCL struggle to generalize across the task formats, and that simple multi-task training fails to improve them.”
   2. UncheckedTask-format-specific control codes and adapters beat the best existing single-embedding model by over 2 points absolute on the SciRepEval benchmark.“We experiment with task-format-specific control codes and adapters and find they outperform the existing single-embedding state-of-the-art by over 2 points absolute.”