DIALOGPT is a large-scale neural conversational response generation model trained on 147 million conversation-like exchanges from Reddit comment chains spanning from 2005 to 2017. It extends the Hugging Face PyTorch transformer to achieve performance close to human in both automatic and human evaluations for single-turn dialogue settings. DIALOGPT generates 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. DIALOGPT is based on the GPT-2 architecture, with modifications to handle dialogue generation. It is trained on large-scale dialogue pairs/sessions extracted from Reddit discussions. The dataset includes 147 million dialogue instances, totaling 1.8 billion words. The model is trained to generate responses that are diverse and contain information specific to the source prompt, similar to what GPT-2 generates for continuous text. The model uses a multi-layer transformer architecture and is formulated as an autoregressive language model. It is trained on a large-scale dataset and evaluated on a public benchmark dataset (DSTC-7) and a new 6k multi-reference test dataset extracted from Reddit postings. DIALOGPT achieves state-of-the-art results in both automatic and human evaluation, lifting performance to near-human response quality. To address the issue of generating bland, uninformative samples, the model implements a maximum mutual information (MMI) scoring function. This helps in re-ranking hypotheses to produce more diverse and informative responses. The model is also evaluated on a new Reddit multi-reference dataset, showing that larger models consistently outperform smaller ones. DIALOGPT is open-sourced and includes a pre-trained model and a training pipeline. It is easy to deploy and can be adapted to new dialogue datasets. The model is capable of generating responses that are more consistent with context and better at handling multi-turn conversations compared to RNN-based models. Human evaluations show that DIALOGPT outperforms other models in terms of relevance, informativeness, and human-likeness. However, there are concerns about potential biases and offensive content generated by the model. The authors emphasize that the model should be used responsibly and that the views or values of the authors or Microsoft Corporation should not be attributed to inappropriate content generated by the model.DIALOGPT is a large-scale neural conversational response generation model trained on 147 million conversation-like exchanges from Reddit comment chains spanning from 2005 to 2017. It extends the Hugging Face PyTorch transformer to achieve performance close to human in both automatic and human evaluations for single-turn dialogue settings. DIALOGPT generates 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. DIALOGPT is based on the GPT-2 architecture, with modifications to handle dialogue generation. It is trained on large-scale dialogue pairs/sessions extracted from Reddit discussions. The dataset includes 147 million dialogue instances, totaling 1.8 billion words. The model is trained to generate responses that are diverse and contain information specific to the source prompt, similar to what GPT-2 generates for continuous text. The model uses a multi-layer transformer architecture and is formulated as an autoregressive language model. It is trained on a large-scale dataset and evaluated on a public benchmark dataset (DSTC-7) and a new 6k multi-reference test dataset extracted from Reddit postings. DIALOGPT achieves state-of-the-art results in both automatic and human evaluation, lifting performance to near-human response quality. To address the issue of generating bland, uninformative samples, the model implements a maximum mutual information (MMI) scoring function. This helps in re-ranking hypotheses to produce more diverse and informative responses. The model is also evaluated on a new Reddit multi-reference dataset, showing that larger models consistently outperform smaller ones. DIALOGPT is open-sourced and includes a pre-trained model and a training pipeline. It is easy to deploy and can be adapted to new dialogue datasets. The model is capable of generating responses that are more consistent with context and better at handling multi-turn conversations compared to RNN-based models. Human evaluations show that DIALOGPT outperforms other models in terms of relevance, informativeness, and human-likeness. However, there are concerns about potential biases and offensive content generated by the model. The authors emphasize that the model should be used responsibly and that the views or values of the authors or Microsoft Corporation should not be attributed to inappropriate content generated by the model.