@inproceedings{zhou-etal-2024-constructions-difficult,
title = "Constructions Are So Difficult That {E}ven Large Language Models Get Them Right for the Wrong Reasons",
author = {Zhou, Shijia and
Weissweiler, Leonie and
He, Taiqi and
Sch{\"u}tze, Hinrich and
Mortensen, David R. and
Levin, Lori},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.336",
pages = "3804--3811",
abstract = "In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create further challenging sub-tasks in an effort to explain this failure. From a Computational Linguistics perspective, we identify a group of constructions with three classes of adjectives which cannot be distinguished by surface features. This enables us to probe for LLM{'}s understanding of these constructions in various ways, and we find that they fail in a variety of ways to distinguish between them, suggesting that they don{'}t adequately represent their meaning or capture the lexical properties of phrasal heads.",
}
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<abstract>In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create further challenging sub-tasks in an effort to explain this failure. From a Computational Linguistics perspective, we identify a group of constructions with three classes of adjectives which cannot be distinguished by surface features. This enables us to probe for LLM’s understanding of these constructions in various ways, and we find that they fail in a variety of ways to distinguish between them, suggesting that they don’t adequately represent their meaning or capture the lexical properties of phrasal heads.</abstract>
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%0 Conference Proceedings
%T Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons
%A Zhou, Shijia
%A Weissweiler, Leonie
%A He, Taiqi
%A Schütze, Hinrich
%A Mortensen, David R.
%A Levin, Lori
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zhou-etal-2024-constructions-difficult
%X In this paper, we make a contribution that can be understood from two perspectives: from an NLP perspective, we introduce a small challenge dataset for NLI with large lexical overlap, which minimises the possibility of models discerning entailment solely based on token distinctions, and show that GPT-4 and Llama 2 fail it with strong bias. We then create further challenging sub-tasks in an effort to explain this failure. From a Computational Linguistics perspective, we identify a group of constructions with three classes of adjectives which cannot be distinguished by surface features. This enables us to probe for LLM’s understanding of these constructions in various ways, and we find that they fail in a variety of ways to distinguish between them, suggesting that they don’t adequately represent their meaning or capture the lexical properties of phrasal heads.
%U https://aclanthology.org/2024.lrec-main.336
%P 3804-3811
Markdown (Informal)
[Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasons](https://aclanthology.org/2024.lrec-main.336) (Zhou et al., LREC-COLING 2024)
ACL