Analysis of the Reliability and Efficiency of Information Extraction Using AI-Based Chatbot: The More for Less Paradox
Eugene Levner, Boris Kriheli
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Source: Crossref
Published: Mar 19, 2025
DOI: 10.20944/preprints202503.1427.v1
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This paper addresses the following problem of information extraction using an AI-powered chatbot. An AI chatbot processes a natural language conversation with a human user using semantic search and human-chatbot interaction to extract the most relevant and contextually appropriate information from available databases. During a natural language conversation between a human and an AI, the human user can modify and refine queries until he/she is satisfied with the chatbot’s output. The reliability of an AI chatbot is defined as its ability to understand user queries and provide correct answers; in the current study, it is measured by the frequency (probability) of correct answers. The search efficiency of a chatbot indicates how accurate and relevant the information returned by the chatbot is; in this work, it is measured by the satisfaction level that the human user receives for a correct answer. We uncover a counterintuitive relationship between AI chatbot reliability and search efficiency: we demonstrate that, under fairly general conditions, a less reliable AI chatbot can have a higher expected search efficiency than a more reliable chatbot. This phenomenon aligns with a family of “more-for-less” paradoxes observed in various complex systems. Finally, we discuss the underlying mechanism of this paradox.
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