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A curated collection of research papers on Large Language Models (LLMs), transformers, prompting, fine-tuning, and multi-modal systems. Maintained by Sean Moran.

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Dialogpt: Large-scale Generative Pre-training For Conversational Response Generation

Yizhe Zhang et al.. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations 2019 – 357 citations

[Paper]    
Model Architecture GPT Transformer RAG Pre-Training Training Techniques Evaluation

We present a large, tunable neural conversational response generation model, DialoGPT (dialogue generative pre-trained transformer). Trained on 147M conversation-like exchanges extracted from Reddit comment chains over a period spanning from 2005 through 2017, DialoGPT extends the Hugging Face PyTorch transformer to attain a performance close to human both in terms of automatic and human evaluation in single-turn dialogue settings. We show that conversational systems that leverage DialoGPT generate more relevant, contentful and context-consistent responses than strong baseline systems. The pre-trained model and training pipeline are publicly released to facilitate research into neural response generation and the development of more intelligent open-domain dialogue systems.

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