@inproceedings{wang-etal-2024-abspyramid,
title = "{A}bs{P}yramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph",
author = "Wang, Zhaowei and
Shi, Haochen and
Wang, Weiqi and
Fang, Tianqing and
Zhang, Hongming and
Choi, Sehyun and
Liu, Xin and
Song, Yangqiu",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.252",
pages = "3991--4010",
abstract = "Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present AbsPyramid, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, AbsPyramid collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks.",
}
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<abstract>Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present AbsPyramid, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, AbsPyramid collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks.</abstract>
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%0 Conference Proceedings
%T AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph
%A Wang, Zhaowei
%A Shi, Haochen
%A Wang, Weiqi
%A Fang, Tianqing
%A Zhang, Hongming
%A Choi, Sehyun
%A Liu, Xin
%A Song, Yangqiu
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F wang-etal-2024-abspyramid
%X Cognitive research indicates that abstraction ability is essential in human intelligence, which remains under-explored in language models. In this paper, we present AbsPyramid, a unified entailment graph of 221K textual descriptions of abstraction knowledge. While existing resources only touch nouns or verbs within simplified events or specific domains, AbsPyramid collects abstract knowledge for three components of diverse events to comprehensively evaluate the abstraction ability of language models in the open domain. Experimental results demonstrate that current LLMs face challenges comprehending abstraction knowledge in zero-shot and few-shot settings. By training on our rich abstraction knowledge, we find LLMs can acquire basic abstraction abilities and generalize to unseen events. In the meantime, we empirically show that our benchmark is comprehensive to enhance LLMs across two previous abstraction tasks.
%U https://aclanthology.org/2024.findings-naacl.252
%P 3991-4010
Markdown (Informal)
[AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph](https://aclanthology.org/2024.findings-naacl.252) (Wang et al., Findings 2024)
ACL
- Zhaowei Wang, Haochen Shi, Weiqi Wang, Tianqing Fang, Hongming Zhang, Sehyun Choi, Xin Liu, and Yangqiu Song. 2024. AbsPyramid: Benchmarking the Abstraction Ability of Language Models with a Unified Entailment Graph. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3991–4010, Mexico City, Mexico. Association for Computational Linguistics.