{
 "id": "fdelucaf/IdioTS",
 "owner": "fdelucaf",
 "name": "IdioTS",
 "downloads": 35,
 "likes": 1,
 "last_modified": "2024-11-29T15:06:34.000Z",
 "stated_licence": "cc-by-nc-sa-4.0",
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 "licence_name": null,
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 "gated": false,
 "tasks": [
  "text-classification",
  "translation"
 ],
 "citation": "@inproceedings{de-luca-fornaciari-etal-2024-hard,\n    title = \"A Hard Nut to Crack: Idiom Detection with Conversational Large Language Models\",\n    author = \"De Luca Fornaciari, Francesca  and\n      Altuna, Bego{\\~n}a  and\n      Gonzalez-Dios, Itziar  and\n      Melero, Maite\",\n    editor = \"Ghosh, Debanjan  and\n      Muresan, Smaranda  and\n      Feldman, Anna  and\n      Chakrabarty, Tuhin  and\n      Liu, Emmy\",\n    booktitle = \"Proceedings of the 4th Workshop on Figurative Language Processing (FigLang 2024)\",\n    month = jun,\n    year = \"2024\",\n    address = \"Mexico City, Mexico (Hybrid)\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2024.figlang-1.5\",\n    doi = \"10.18653/v1/2024.figlang-1.5\",\n    pages = \"35--44\",\n    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.\",\n}",
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 "reason": "The declared licence (cc-by-nc-sa-4.0) is classified as having use restrictions.",
 "revision": "bb6c0eb4d7c2925be02bd54eb115d2a448dac32c",
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