@inproceedings{zugarini-etal-2024-ecwca,
title = "{ECWCA} - Educational {C}ross{W}ord Clues Answering: A {CALAMITA} Challenge",
author = "Zugarini, Andrea and
Zeinalipour, Kamyar and
Fusco, Achille and
Zanollo, Asya",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.138/",
pages = "1239--1244",
ISBN = "979-12-210-7060-6",
abstract = "This paper presents ECWCA (Educational CrossWord Clues Answering), a novel challenge designed to evaluate knowledge and reasoning capabilities of large language models through crossword clue-answering. The challenge consists of two tasks: a standard question-answering format where the LLM has to solve crossword clues, and a variation of it, where the model is receives hints about the word lengths of the answers, which is expected to help models with reasoning abilities. To construct the ECWCA dataset, synthetic clues were generated based on entities and facts extracted from Italian Wikipedia. Generated clues were then selected manually in order to ensure high-quality examples with factually correct and unambiguous clues."
}
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<abstract>This paper presents ECWCA (Educational CrossWord Clues Answering), a novel challenge designed to evaluate knowledge and reasoning capabilities of large language models through crossword clue-answering. The challenge consists of two tasks: a standard question-answering format where the LLM has to solve crossword clues, and a variation of it, where the model is receives hints about the word lengths of the answers, which is expected to help models with reasoning abilities. To construct the ECWCA dataset, synthetic clues were generated based on entities and facts extracted from Italian Wikipedia. Generated clues were then selected manually in order to ensure high-quality examples with factually correct and unambiguous clues.</abstract>
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%0 Conference Proceedings
%T ECWCA - Educational CrossWord Clues Answering: A CALAMITA Challenge
%A Zugarini, Andrea
%A Zeinalipour, Kamyar
%A Fusco, Achille
%A Zanollo, Asya
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F zugarini-etal-2024-ecwca
%X This paper presents ECWCA (Educational CrossWord Clues Answering), a novel challenge designed to evaluate knowledge and reasoning capabilities of large language models through crossword clue-answering. The challenge consists of two tasks: a standard question-answering format where the LLM has to solve crossword clues, and a variation of it, where the model is receives hints about the word lengths of the answers, which is expected to help models with reasoning abilities. To construct the ECWCA dataset, synthetic clues were generated based on entities and facts extracted from Italian Wikipedia. Generated clues were then selected manually in order to ensure high-quality examples with factually correct and unambiguous clues.
%U https://aclanthology.org/2024.clicit-1.138/
%P 1239-1244
Markdown (Informal)
[ECWCA - Educational CrossWord Clues Answering: A CALAMITA Challenge](https://aclanthology.org/2024.clicit-1.138/) (Zugarini et al., CLiC-it 2024)
ACL