How AI Chatbot works: Simplifying the Magic of Conversational AI

Hari Kiran Vuyyuru

doi:10.32628/CSEIT25111273

Authors

Hari Kiran Vuyyuru Texas A&M University, USA Author

DOI:

https://doi.org/10.32628/CSEIT25111273

Keywords:

Large Language Models, Natural Language Processing, Token Prediction, Reinforcement Learning, Context Management

Abstract

This comprehensive article explores the fundamental mechanisms and capabilities of Generative AI, examining how this advanced language model achieves human-like conversational abilities. The article delves into the system's core components, including its sophisticated tokenization processes, context management mechanisms, next-token prediction capabilities, and training methodologies enhanced through human feedback. Through a detailed analysis of recent research findings, the article demonstrates how Generative AI chatbot transcends simple pattern matching to achieve complex reasoning, creative generation, and adaptive communication abilities across various specialized domains. Special attention is given to the model's applications in healthcare, education, and enterprise settings, highlighting its remarkable achievements and inherent limitations compared to human cognition.

Downloads

Download data is not yet available.

References

Siqi Wang et al., "Scaling Laws Across Model Architectures: A Comparative Analysis of Dense and MoE Models in Large Language Models," arXiv:2410.05661 [cs.LG], 8 Oct 2024. [Online]. Available: https://arxiv.org/abs/2410.05661

Zhehui Wang et al., "Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small," arXiv:2410.15977v1 [cs.AI] 21 Oct 2024. [Online]. Available: https://arxiv.org/pdf/2410.15977

S. Tamang, D. J. Bora, "Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages," arXiv:2411.12240 [cs.CL], 26 Nov 2024. [Online]. Available: https://arxiv.org/abs/2411.12240

Christopher Keith et al., "Optimizing Large Language Models: A Novel Approach Through Dynamic Token Pruning," Research Square, 21 Oct 2024. [Online]. Available: https://www.researchsquare.com/article/rs-5293588/v1

Rongsheng Li et al., "Extending Context Window in Large Language Models with Segmented Base Adjustment for Rotary Position Embeddings," Appl. Sci. 2024, 14(7), 3076, 6 April 2024. [Online]. Available: https://www.mdpi.com/2076-3417/14/7/3076

Viktor Lapov, "Dynamic Context Integration in Large Language Models Using a Novel Progressive Layering Framework," Research Square, 30 Oct 2024. [Online]. Available: https://www.researchsquare.com/article/rs-5357232/v1

Hangfeng He, Weijie J. Su, "A Law of Next-Token Prediction in Large Language Models," arXiv:2408.13442 [cs.LG], 24 Aug 2024. [Online]. Available: https://arxiv.org/abs/2408.13442

Fabian Gloeckle et al., "Better & Faster Large Language Models via Multi-token Prediction," arXiv:2404.19737v1 [cs.CL] 30 Apr 2024. [Online]. Available: https://arxiv.org/pdf/2404.19737

Dave Bergmann, "What is reinforcement learning from human feedback (RLHF)?," IBM, 10 November 2023. [Online]. Available: https://www.ibm.com/think/topics/rlhf

Shreyas Chaudhari et al., "RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs," arXiv:2404.08555 [cs.LG], 16 Apr 2024. [Online]. Available: https://arxiv.org/abs/2404.08555

David Ilić, Gilles E. Gignac, "Evidence of interrelated cognitive-like capabilities in large language models: Indications of artificial general intelligence or achievement?" Intelligence, Volume 106, September–October 2024, 101858. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0160289624000527

Tianshi Zheng et al., "CLR-Fact: Evaluating the Complex Logical Reasoning Capability of Large Language Models over Factual Knowledge," arXiv:2407.20564 [cs.CL], 30 Jul 2024. [Online]. Available: https://arxiv.org/abs/2407.20564