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 [BibTeX] [Marc21]
Can Language Models Learn Analogical Reasoning? Investigating Training Objectives and Comparisons to Human Performance
Type of publication: Conference paper
Citation: Petersen_EMNLP2023_2023
Publication status: Accepted
Booktitle: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Year: 2023
Month: December
Publisher: Association for Computational Linguistics
Location: Singapore
Abstract: While analogies are a common way to evaluate word embeddings in NLP, it is also of interest to investigate whether or not analogical reasoning is a task in itself that can be learned. In this paper, we test several ways to learn basic analogical reasoning, specifically focusing on analogies that are more typical of what is used to evaluate analogical reasoning in humans than those in commonly used NLP benchmarks. Our experiments find that models are able to learn analogical reasoning, even with a small amount of data. We additionally compare our models to a dataset with a human baseline, and find that after training, models approach human performance.
Keywords: analogical reasoning, NLP
Projects Idiap
Authors Petersen, Molly R.
van der Plas, Lonneke
Added by: [UNK]
Total mark: 0
  • Petersen_EMNLP2023_2023.pdf