As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language
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Computer Science > Computation and Language
Title:As Easy as Rocket Science: Assessing the Ability of Large Language Models to Interpret Negation in Figurative Language
Abstract:Figurative language and negation are two areas that challenge current language models, however, both are widely used throughout written and spoken language. Large language models (LLMs) are also widely used in everyday contexts where they cannot necessarily be tuned for a specific dataset. It is therefore essential to understand the ability of LLMs to correctly interpret text that includes both negation and figurative language. To investigate this, we develop a set of new annotations to an existing dataset of figurative language, and test a range of language models on the dataset. We find that the combination of negation and figurativeness can present a particular challenge, and that performance overall and across different negation types is particularly dependent on the prompt style used.
| Comments: | 16 pages, 16 figures; for associated code and data see this https URL To be published in Transactions of the Association for Computational Linguistics |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| MSC classes: | 68T50 |
| ACM classes: | I.2.7 |
| Cite as: | arXiv:2606.18922 [cs.CL] |
| (or arXiv:2606.18922v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18922
arXiv-issued DOI via DataCite (pending registration)
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