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ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models

Jentzsch, Sophie Freya und Kersting, Kristian (2023) ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models. In: 61st Annual Meeting of the Association for Computational Linguistics, ACL 2023. 13th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, 2023-07-09 - 2023-07-14, Toronto, Canada. doi: 10.48550/arXiv.2306.04563. ISBN 978-195942987-6. ISSN 0736-587X.

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Kurzfassung

Humor is a central aspect of human communication that has not been solved for artificial agents so far. Large language models (LLMs) are increasingly able to capture implicit and contextual information. Especially, OpenAI's ChatGPT recently gained immense public attention. The GPT3-based model almost seems to communicate on a human level and can even tell jokes. Humor is an essential component of human communication. But is ChatGPT really funny? We put ChatGPT's sense of humor to the test. In a series of exploratory experiments around jokes, i.e., generation, explanation, and detection, we seek to understand ChatGPT's capability to grasp and reproduce human humor. Since the model itself is not accessible, we applied prompt-based experiments. Our empirical evidence indicates that jokes are not hard-coded but mostly also not newly generated by the model. Over 90% of 1008 generated jokes were the same 25 Jokes. The system accurately explains valid jokes but also comes up with fictional explanations for invalid jokes. Joke-typical characteristics can mislead ChatGPT in the classification of jokes. ChatGPT has not solved computational humor yet but it can be a big leap toward "funny" machines.

elib-URL des Eintrags:https://elib.dlr.de/198942/
Dokumentart:Konferenzbeitrag (Poster)
Titel:ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Jentzsch, Sophie FreyaSophie.Jentzsch (at) dlr.dehttps://orcid.org/0000-0001-6217-8814NICHT SPEZIFIZIERT
Kersting, KristianDarmstadt University of TechnologyNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:Juli 2023
Erschienen in:61st Annual Meeting of the Association for Computational Linguistics, ACL 2023
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Nein
DOI:10.48550/arXiv.2306.04563
ISSN:0736-587X
ISBN:978-195942987-6
Status:veröffentlicht
Stichwörter:rtificial Intelligence (cs.AI); Computation and Language (cs.CL); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Veranstaltungstitel:13th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
Veranstaltungsort:Toronto, Canada
Veranstaltungsart:Workshop
Veranstaltungsbeginn:9 Juli 2023
Veranstaltungsende:14 Juli 2023
Veranstalter :ACL
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrtsysteme
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - Aufgaben SISTEC
Standort: Köln-Porz
Institute & Einrichtungen:Institut für Softwaretechnologie
Institut für Softwaretechnologie > Intelligente und verteilte Systeme
Hinterlegt von: Jentzsch, Sophie Freya
Hinterlegt am:14 Nov 2023 08:47
Letzte Änderung:24 Apr 2024 20:59

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