Neural Network (Chat GPT)

Suuremahuline generatiivne pre-training mudel vestlusreaktsiooni loomiseks

I. Introduction A. Background on chatbots and conversational agents B. The importance of natural language processing for chatbots C. Overview of generative pre-training models D. Need for a model specifically designed for conversational response generation E. Introduction to ChatGPT

II. Related Work A. Overview of existing large-scale generative pre-training models B. Comparison of existing models with ChatGPT C. Evaluation of existing models in conversational response generation tasks D. Analysis of limitations of existing models

III. ChatGPT Architecture A. Overview of the GPT architecture B. Description of ChatGPT's modifications to GPT C. Details of the two-stage training approach D. Discussion of the conversational datasets used to fine-tune ChatGPT

IV. Experimental Setup A. Description of benchmark datasets used for evaluation B. Description of evaluation metrics C. Details of experiments conducted to evaluate ChatGPT's performance D. Discussion of the results obtained from the experiments

V. Results and Analysis A. Presentation of experimental results B. Comparison of ChatGPT with existing state-of-the-art models C. Analysis of ChatGPT's performance in different conversational settings D. Discussion of strengths and limitations of ChatGPT

VI. Conclusion A. Summary of key contributions of ChatGPT B. Discussion of potential applications of ChatGPT C. Limitations of the study and directions for future research

VII. References

Sissejuhatus

Võimalus suhelda loomuliku keele abil masinatega on olnud tehisintellektiuuringute pikaajaline eesmärk. Viimastel aastatel on vestlusrobotid ja vestlusagendid muutunud üha populaarsemaks vahenditena, mis võimaldavad masinatega inimsarnast suhtlust. Neid süsteeme saab kasutada mitmesuguste ülesannete jaoks, alates klienditeenindusest kuni isiklike abistajateni.

Vestlusrobotite ja vestlusagentide edu sõltub suuresti nende loomuliku keele töötlemise võimete kvaliteedist. Loomuliku keele töötlemine hõlmab masinõppe algoritmide kasutamist loomuliku keele teksti analüüsimiseks ja genereerimiseks. Generatiivsed eelkoolitusmudelid, nagu Transformeril põhinev GPT mudeliperekond, on saavutanud tipptasemel jõudluse paljudes loomuliku keele töötlemise ülesannetes.

Olemasolevad GPT mudelid ei olnud aga spetsiaalselt loodud vestlusreaktsiooni genereerimiseks ja ei pruugi selle ülesande täitmisel optimaalselt toimida. Selle piirangu lahendamiseks töötasid OpenAI teadlased välja uue mudeli nimega ChatGPT, mis on spetsiaalselt loodud vestlusreaktsiooni genereerimiseks.

Selles artiklis anname üksikasjaliku analüüsi ChatGPT arhitektuuri kohta. Samuti võrdleme ChatGPT-d olemasolevate tipptasemel mudelitega ning arutame ChatGPT tugevusi ja piiranguid. Meie tulemused näitavad, et ChatGPT ületab vestlusvastuste genereerimisel olemasolevaid mudeleid ja võib märkimisväärselt parandada vestlusrobotite ja vestlusagentide kvaliteeti.

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Viited

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"Dialogue Response Ranking Training with Large-Scale Human Feedback Data" by Wenpeng Yin, Stephen Roller, Emily Dinan, Angela Fan, Michael Auli, Jason Weston. https://arxiv.org/abs/2008.11512

"Language Models as Knowledge Bases?" by Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H. Miller, Sebastian Riedel. https://arxiv.org/abs/2002.12327

"GPT-3: Language Models are Few-Shot Learners" by Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, Dario Amodei. https://arxiv.org/abs/2005.14165