Beyond Probabilities: Unveiling the Delicate Dance of Large Language Models (LLMs) and AI-Hallucination

Large language models (LLMs), like OpenAI's ChatGPT and Google's Gemini, operate as probabilistic models, leveraging their ability to generalise and discern intricate patterns within data. By assigning probabilities to different tokens based on patterns learned during extensive training on large datasets, these models can generate a wide range of contextually appropriate responses, spanning from textual scripts to auditory and visual outputs (both static and moving images). However, the inherent probabilistic nature of LLMs introduces a notable challenge, leading to the phenomenon known in the field of artificial intelligence as 'AI-hallucination,' where the model may produce responses that sound plausible but are factually incorrect or nonsensical. Despite being perceived as a drawback, we posit in this paper that AI-hallucinations can be reframed as a distinctive feature of LLMs rather than a mere limitation. Our argument stems from the understanding that attempts to mitigate the harms caused by AI-hallucinations might inadvertently lead to increased model rigidity. This delicate balance between minimising harm and preserving the model's flexibility is a central theme in our discussion. Furthermore, we revisit the concept of 'context,' contending that a complete definition goes beyond the mere description of circumstances, environment, or surrounding facts. We assert that context is enriched by a conscious embodiment, involving the choice or refusal of action (considering all associate ethical implications) among a set of available options.