Is word order considered by foundation models? A comparative task-oriented analysis

Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct ab...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhao, Qinghua [verfasserIn] Li, Jiaang [verfasserIn] Liu, Junfeng [verfasserIn] Kang, Zhongfeng [verfasserIn] Zhou, Zenghui [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Foundation model Word order Order perturbation ratio MGSM WinoGrande

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 241
Übergeordnetes Werk:	volume:241

DOI / URN:	10.1016/j.eswa.2023.122700

Katalog-ID:	ELV066975182

Internformat


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245	1	0	\|a Is word order considered by foundation models? A comparative task-oriented analysis
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520			\|a Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge.
650		4	\|a Foundation model
650		4	\|a Word order
650		4	\|a Order perturbation ratio
650		4	\|a MGSM
650		4	\|a WinoGrande
700	1		\|a Li, Jiaang \|e verfasserin \|0 (orcid)0009-0000-5656-6204 \|4 aut
700	1		\|a Liu, Junfeng \|e verfasserin \|0 (orcid)0009-0006-8205-4564 \|4 aut
700	1		\|a Kang, Zhongfeng \|e verfasserin \|0 (orcid)0000-0001-9025-0748 \|4 aut
700	1		\|a Zhou, Zenghui \|e verfasserin \|0 (orcid)0000-0002-1824-6979 \|4 aut
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936	b	k	\|a 54.72 \|j Künstliche Intelligenz \|q VZ
951			\|a AR
952			\|d 241

Indexfelder

author_variant	q z qz j l jl j l jl z k zk z z zz
matchkey_str	zhaoqinghualijiaangliujunfengkangzhongfe:2023----:sodrecnieebfudtomdlaoprtv
hierarchy_sort_str	2023
bklnumber	54.72
publishDate	2023
allfields	10.1016/j.eswa.2023.122700 doi (DE-627)ELV066975182 (ELSEVIER)S0957-4174(23)03202-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhao, Qinghua verfasserin (orcid)0000-0003-4906-7049 aut Is word order considered by foundation models? A comparative task-oriented analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge. Foundation model Word order Order perturbation ratio MGSM WinoGrande Li, Jiaang verfasserin (orcid)0009-0000-5656-6204 aut Liu, Junfeng verfasserin (orcid)0009-0006-8205-4564 aut Kang, Zhongfeng verfasserin (orcid)0000-0001-9025-0748 aut Zhou, Zenghui verfasserin (orcid)0000-0002-1824-6979 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241
spelling	10.1016/j.eswa.2023.122700 doi (DE-627)ELV066975182 (ELSEVIER)S0957-4174(23)03202-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhao, Qinghua verfasserin (orcid)0000-0003-4906-7049 aut Is word order considered by foundation models? A comparative task-oriented analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge. Foundation model Word order Order perturbation ratio MGSM WinoGrande Li, Jiaang verfasserin (orcid)0009-0000-5656-6204 aut Liu, Junfeng verfasserin (orcid)0009-0006-8205-4564 aut Kang, Zhongfeng verfasserin (orcid)0000-0001-9025-0748 aut Zhou, Zenghui verfasserin (orcid)0000-0002-1824-6979 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241
allfields_unstemmed	10.1016/j.eswa.2023.122700 doi (DE-627)ELV066975182 (ELSEVIER)S0957-4174(23)03202-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhao, Qinghua verfasserin (orcid)0000-0003-4906-7049 aut Is word order considered by foundation models? A comparative task-oriented analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge. Foundation model Word order Order perturbation ratio MGSM WinoGrande Li, Jiaang verfasserin (orcid)0009-0000-5656-6204 aut Liu, Junfeng verfasserin (orcid)0009-0006-8205-4564 aut Kang, Zhongfeng verfasserin (orcid)0000-0001-9025-0748 aut Zhou, Zenghui verfasserin (orcid)0000-0002-1824-6979 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241
allfieldsGer	10.1016/j.eswa.2023.122700 doi (DE-627)ELV066975182 (ELSEVIER)S0957-4174(23)03202-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhao, Qinghua verfasserin (orcid)0000-0003-4906-7049 aut Is word order considered by foundation models? A comparative task-oriented analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge. Foundation model Word order Order perturbation ratio MGSM WinoGrande Li, Jiaang verfasserin (orcid)0009-0000-5656-6204 aut Liu, Junfeng verfasserin (orcid)0009-0006-8205-4564 aut Kang, Zhongfeng verfasserin (orcid)0000-0001-9025-0748 aut Zhou, Zenghui verfasserin (orcid)0000-0002-1824-6979 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241
allfieldsSound	10.1016/j.eswa.2023.122700 doi (DE-627)ELV066975182 (ELSEVIER)S0957-4174(23)03202-5 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Zhao, Qinghua verfasserin (orcid)0000-0003-4906-7049 aut Is word order considered by foundation models? A comparative task-oriented analysis 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge. Foundation model Word order Order perturbation ratio MGSM WinoGrande Li, Jiaang verfasserin (orcid)0009-0000-5656-6204 aut Liu, Junfeng verfasserin (orcid)0009-0006-8205-4564 aut Kang, Zhongfeng verfasserin (orcid)0000-0001-9025-0748 aut Zhou, Zenghui verfasserin (orcid)0000-0002-1824-6979 aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 241 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:241 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 241
language	English
source	Enthalten in Expert systems with applications 241 volume:241
sourceStr	Enthalten in Expert systems with applications 241 volume:241
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Foundation model Word order Order perturbation ratio MGSM WinoGrande
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Zhao, Qinghua @@aut@@ Li, Jiaang @@aut@@ Liu, Junfeng @@aut@@ Kang, Zhongfeng @@aut@@ Zhou, Zenghui @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV066975182
language_de	englisch
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author	Zhao, Qinghua
spellingShingle	Zhao, Qinghua ddc 004 bkl 54.72 misc Foundation model misc Word order misc Order perturbation ratio misc MGSM misc WinoGrande Is word order considered by foundation models? A comparative task-oriented analysis
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topic_title	004 VZ 54.72 bkl Is word order considered by foundation models? A comparative task-oriented analysis Foundation model Word order Order perturbation ratio MGSM WinoGrande
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title_sort	is word order considered by foundation models? a comparative task-oriented analysis
title_auth	Is word order considered by foundation models? A comparative task-oriented analysis
abstract	Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge.
abstractGer	Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge.
abstract_unstemmed	Word order, a linguistic concept essential for conveying accurate meaning, is seemingly not that necessary in language models based on the existing works. Contrary to this prevailing notion, our paper delves into the impacts of word order by employing carefully selected tasks that demand distinct abilities. Using three large language model families (ChatGPT, Claude, LLaMA), three controllable word order perturbation strategies, one novel perturbation qualification metric, four well-chosen tasks, and three languages, we conduct experiments to shed light on this topic. Empirical findings demonstrate that Foundation models take word order into consideration during generation. Moreover, tasks emphasizing reasoning abilities exhibit a greater reliance on word order compared to those primarily based on world knowledge.
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title_short	Is word order considered by foundation models? A comparative task-oriented analysis
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author2	Li, Jiaang Liu, Junfeng Kang, Zhongfeng Zhou, Zenghui
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up_date	2024-07-06T19:39:06.848Z
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