Testing the Ability of Language Models to Interpret Figurative Language

Anonymous

Paper Link: https://openreview.net/forum?id=lkDt1ZHKaj7

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

Abstract: Figurative and metaphorical language are commonplace in discourse, and figurative expressions play an important role in communication and cognition. However, figurative language has been a relatively under-studied area in NLP, and it remains an open question to what extent modern language models can interpret nonliteral phrases. To address this question, we introduce Fig-QA, a Winograd-style nonliteral language understanding task consisting of correctly interpreting paired figurative phrases with divergent meanings. We evaluate the performance of several state-of-the-art language models on this task, and find that although language models achieve performance significantly over chance, they still fall short of human performance, particularly in zero- or few-shot settings. This suggests that further work is needed to improve the nonliteral reasoning capabilities of language models.

Presentation Mode: This paper will be presented in person in Seattle

Copyright Consent Signature (type Name Or NA If Not Transferrable): Meng Yan Liu

Copyright Consent Name And Address: Carnegie Mellon University, 5000 Forbes Ave