LLM Asynchronous Processing: Reality or Hallucination?

LLM Asynchronous Processing: Reality or Hallucination?

Large language models (LLMs) such as Gemini and ChatGPT have ignited interest in their capacity to manage complex tasks, especially when they seem to indicate ongoing background processing. This article delves into the truth behind the responses of these AI models to complex prompts, examining whether they genuinely have asynchronous processing abilities or if such actions are simply persuasive simulations derived from their training.

Illusion of Continuous Activity

Contrary to what it may seem, LLMs like Gemini and ChatGPT lack actual asynchronous or background processing capabilities. These models function on a request-response mechanism, producing outputs that depend entirely on the input provided at that specific time. When they imply that they are "processing" or will "return with results," it is, in reality, a type of confabulation, not actual continuous computation. The semblance of ongoing work is due to the models' ability to maintain conversational coherence. Nonetheless, each interaction is discrete, and the LLM does not preserve information from one exchange to the next unless it is included in the conversation history. This absence of state implies that when immediate results are requested after a suggested delay, the model merely formulates a new response based on the most recent prompt, rather than accessing any processed data.

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