@inproceedings{de-luca-fornaciari-etal-2024-hard,
title = "A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models",
author = "De Luca Fornaciari, Francesca and
Altuna, Bego{\~n}a and
Gonzalez-Dios, Itziar and
Melero, Maite",
editor = "Ghosh, Debanjan and
Muresan, Smaranda and
Feldman, Anna and
Chakrabarty, Tuhin and
Liu, Emmy",
booktitle = "Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.figlang-1.5",
doi = "10.18653/v1/2024.figlang-1.5",
pages = "35--44",
abstract = "In this work, we explore idiomatic language processing with Large Language Models (LLMs). We introduce the Idiomatic language Test Suite IdioTS, a dataset of difficult examples specifically designed by language experts to assess the capabilities of LLMs to process figurative language at sentence level. We propose a comprehensive evaluation methodology based on an idiom detection task, where LLMs are prompted with detecting an idiomatic expression in a given English sentence. We present a thorough automatic and manual evaluation of the results and a comprehensive error analysis.",
}
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%0 Conference Proceedings
%T A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models
%A De Luca Fornaciari, Francesca
%A Altuna, Begoña
%A Gonzalez-Dios, Itziar
%A Melero, Maite
%Y Ghosh, Debanjan
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Chakrabarty, Tuhin
%Y Liu, Emmy
%S Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico (Hybrid)
%F de-luca-fornaciari-etal-2024-hard
%X In this work, we explore idiomatic language processing with Large Language Models (LLMs). We introduce the Idiomatic language Test Suite IdioTS, a dataset of difficult examples specifically designed by language experts to assess the capabilities of LLMs to process figurative language at sentence level. We propose a comprehensive evaluation methodology based on an idiom detection task, where LLMs are prompted with detecting an idiomatic expression in a given English sentence. We present a thorough automatic and manual evaluation of the results and a comprehensive error analysis.
%R 10.18653/v1/2024.figlang-1.5
%U https://aclanthology.org/2024.figlang-1.5
%U https://doi.org/10.18653/v1/2024.figlang-1.5
%P 35-44
Markdown (Informal)
[A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models](https://aclanthology.org/2024.figlang-1.5) (De Luca Fornaciari et al., Fig-Lang-WS 2024)
ACL