Critical Understanding of LLM-Generated Statements
Reeshabh Choudhary
Reeshabh Choudhary, Senior Technical Architect, Department of Automation COE, Company Name: Eversana, India.
Manuscript received on 03 September 2025 | First Revised Manuscript received on 19 September 2025 | Second Revised Manuscript received on 21 September 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025 | PP: 1-3 | Volume-5 Issue-6, October 2025 | Retrieval Number: 100.1/ijainn.F110505061025 | DOI: 10.54105/ijainn.F1105.05061025
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now that we live in a world where most of the text in recent online interactions that we come across seems to be generated by LLMs, it becomes critical to understand the nature of statements being generated by LLMs. Technology has always been sold to humans under the tag that it is foolproof and will make lives easier. LLMs produce text by predicting the next token or sequence based on probabilities derived from their training data. A question then arises, whether they generate a ‘probability statement’ or ‘probability of a statement’. The difference between the two may seem elusive, but it is actually quite obvious. This paper intends to bring forward that difference to its audience, who, in turn, can understand the capabilities of the machine they are using and adapt a better framework to judge and use the response generated by LLM models in their applications.
Keywords: Artificial Intelligence, Large-Language Models, Probability, Human Judgment, Intelligence.
Scope of the Article: Reasoning and Inference