This perspective paper proposes the D-LLM framework, which couples the authors' GRAPHYP knowledge graphs with LLMs to personalize AI responses and make reasoning traceable, but no empirical validation is presented.
The BS-meter: A ChatGPT-Trained Instrument to Detect Sloppy Language-Games
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abstract
What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, we analyse the distinctive character of language generated by ChatGPT, in relation to questions raised by natural language processing pioneer, and student of Wittgenstein, Margaret Masterman. Following frequent complaints that LLM-based chatbots produce "slop," or even "bullshit," in the sense of Frankfurt's popular monograph On Bullshit, we conduct an empirical study to contrast the language of 1,000 scientific publications with typical text generated by ChatGPT. We then explore whether the same language features can be detected in two well-known contexts of social dysfunction: George Orwell's critique of political speech, and David Graeber's characterisation of bullshit jobs. Using simple hypothesis-testing methods, we demonstrate that a statistical model of sloppy bullshit can reliably relate the Frankfurtian artificial bullshit of ChatGPT to the political and workplace functions of bullshit as observed in natural human language.
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cs.AI 1years
2025 1verdicts
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Matching Game Preferences Through Dialogical Large Language Models: A Perspective
This perspective paper proposes the D-LLM framework, which couples the authors' GRAPHYP knowledge graphs with LLMs to personalize AI responses and make reasoning traceable, but no empirical validation is presented.