The podcast explores the limitations of ChatGPT in creating consistent languages. It examines the arguments and methods used to generate languages, discussing noun cases, skepticism about adjective agreement, and the critique of descriptive language. Inconsistencies in the constructed language Vivencia are highlighted. The concept of Zipfian distribution and its application to language generation is explored, emphasizing its limitations. The podcast concludes by discussing the limitations of ChatGPT in creating languages and the need for consistent word meanings and grammatical structures.
Chat GPT is not designed for creating coherent languages and lacks the understanding of word meaning and creativity necessary for language development.
The genlangs created with Chat GPT, including Voxfera, Vivencia, and LumaVoxa, exhibited inconsistencies and contradictions in their grammar, word usage, translations, and adherence to claimed structures.
Deep dives
The Limitations of Chat GPT in Language Generation
Chat GPT, while capable of generating text, is not designed for creating coherent languages. The AI model has limitations in generating a consistent and meaningful language system. Experimentation by conlang enthusiasts showed that although Chat GPT could provide a description of a language, it struggled to produce accurate translations or adhere to consistent grammar rules. The AI lacked a mechanism for holding a coherent language system in memory, leading to errors and inconsistencies in its output. The text it generated appeared as vaguely language-shaped gibberish rather than fully functional languages. Claims that the genlangs were coherent and consistent were debunked through an analysis of the text, which showed discrepancies and contradictions. The study also revealed that the genlangs did not follow a zypfian distribution, despite claims made in the paper. In conclusion, Chat GPT should not be considered a reliable tool for conlang creation, as it fundamentally lacks the understanding of word meaning and the creativity necessary for language development.
Analyzing the Generated Genlangs: Voxfera
One of the genlangs created with Chat GPT is Voxfera. However, an analysis of Voxfera's grammar and examples revealed inconsistencies and contradictions. The grammar notes mentioned that nouns, adjectives, and verbs were inflected for case, gender, and number. However, verbs should not be inflected for case, as case is a grammatical category related to nouns' syntactic relationship. While the grammar notes mentioned that adjectives agreed with nouns in case, gender, and number, the actual examples did not confirm this. The phonological description of Voxfera lacked concrete details and did not explain pronunciation, making it difficult to interpret. The use of poetic but vague language in Voxfera's grammar notes made it challenging to understand the language's actual structure and elements. When comparing the translated text to the English version, discrepancies were evident, casting doubt on the coherence and consistency of Voxfera as a language.
Evaluating the Genlang: Vivencia
Another genlang generated with Chat GPT is Vivencia. However, an analysis of its grammar and examples indicated inconsistencies and contradictions. While the grammar notes claimed that Vivencia favored a constant vowel syllable structure, the examples showed multiple instances of consonant clusters and coda consonants, indicating a deviation from the claimed structure. The use of tense markers and logical connectors in Vivencia also lacked clarity and consistency in the examples provided. Moreover, the translation of Vivencia's text did not match the claimed translation, raising questions about the accuracy and coherence of the language. The lack of clear evidence for grammatical features and inconsistencies in word usage further challenged the notion that Vivencia was a coherent and consistent language.
Examining LumaVoxa and Overall Findings
LumaVoxa, another genlang created by Chat GPT, also faced scrutiny regarding its consistency and coherence. The gender system described in LumaVoxa's grammar notes could not be confirmed in the examples, and the prefixes for noun cases and plural marking did not consistently align with the translations. Inconsistent word meanings and grammar structures hindered the decipherability of LumaVoxa. A broader analysis of the generated genlangs revealed that none of them displayed the consistency and coherence necessary for a fully functional language. The text produced by Chat GPT was often at odds with the supposed grammar and rules, resulting in vaguely language-shaped gibberish rather than meaningful languages. While the genlangs vaguely followed a zypfian distribution, it is a basic expectation and does not prove their validity as languages. In conclusion, Chat GPT is not a reliable tool for generating coherent and functional conlangs.
De Marzo, Giordano, Francesco Sylos Labini, and Luciano Pietronero. “Zipf’s Law for Cosmic Structures: How Large Are the Greatest Structures in the Universe?” Astronomy & Astrophysics 651 (July 2021): A114. https://doi.org/10.1051/0004-6361/202141081.
Gabaix, X. “Zipf’s Law for Cities: An Explanation.” The Quarterly Journal of Economics 114, no. 3 (August 1, 1999): 739–67. https://doi.org/10.1162/003355399556133.
Li, Wentian. “Analyses of Baby Name Popularity Distribution in U.S. for the Last 131 Years.” Complexity 18, no. 1 (September 2012): 44–50. https://doi.org/10.1002/cplx.21409.
Wang, Ding, Haibo Cheng, Ping Wang, Xinyi Huang, and Gaopeng Jian. “Zipf’s Law in Passwords.” IEEE Transactions on Information Forensics and Security 12, no. 11 (November 2017): 2776–91. https://doi.org/10.1109/TIFS.2017.2721359.
Links
The Tweet that stared this: https://twitter.com/JPSoucy/status/1638747703332175872?s=20 Genlangs and Zipf’s law data https://github.com/Justin-Diamond/genlangs-and-zipfs
Law brief article https://www.reuters.com/legal/new-york-lawyers-sanctioned-using-fake-chatgpt-cases-legal-brief-2023-06-22/
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