Some hypotheses on how chatbots work in problem-solving-driven conversations. Large Language Models as confirmation of the Innovation Illusion
arXiv:2606.07722v1 Announce Type: new Abstract: This article offers a perspective on the nature of chatbots as genuine conversation partners when discussing problems in relation to their solutions. What can chatbots do and what can't they do, and how can this be explained? Our argument draws on Aggregation Dynamics, Cognitive Linguistics, Neuropsychology and Psychology. Our argument focuses on basic chatbots in the hope of thereby making statements about the core functionality of more advanced chatbots. Basic chatbots are assumed to consist of a Large Language Model (LLM) with a simple interfa
The paper is published amidst a rapid proliferation of generative AI applications, prompting critical examination of their fundamental capabilities and limitations.
A deeper understanding of chatbot limitations in problem-solving will inform deployment strategies and manage expectations for AI's role in complex cognitive tasks.
The focus shifts towards understanding the core cognitive functions of LLMs and their inherent limitations, rather than solely focusing on their generative capabilities.
- · AI researchers
- · Developers of specialized AI tools
- · Businesses with nuanced problem-solving needs
- · Vendors overpromising generalized AI problem-solving
- · Users with unrealistic expectations of current chatbots
Increased research into hybrid AI architectures that combine LLMs with other symbolic or reasoning systems.
Greater emphasis on human-in-the-loop systems for critical problem-solving where LLMs are used as intelligent assistants rather than autonomous agents.
Potential for a 'second AI winter' in generalized problem-solving if the 'Innovation Illusion' proves widely impactful on investment and public perception.
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Read at arXiv cs.AI