Chatbots have transformed human-computer interaction by enabling natural language conversations across customer service, mental health, education, and entertainment. This paper traces the evolution from rule-based systems (ELIZA, 1966) to modern large language models (LLMs) like GPT-4. We analyze core architectures including retrieval-based vs. generative models, transformer networks, and reinforcement learning from human feedback (RLHF). Key applications are evaluated alongside persistent challenges: bias, hallucination, privacy, and ethical deployment. Findings indicate that while chatbots achieve human-like fluency in constrained tasks, general intelligence and common-sense reasoning remain incomplete.
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