REVIEW 2 cited by
DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
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.
Forward citations
Cited by 2 Pith papers
-
Do LLM Modules Generalize? A Study on Motion Generation for Autonomous Driving
On Waymo Sim Agents, LLM-style tokenization, positional embeddings, pretraining, RL post-training, and test-time search can be adapted to improve motion generation, but not all transfer without domain-specific changes.
-
Enhancing Medical Dialogue Generation through Knowledge Refinement and Dynamic Prompt Adjustment
MedRef combines variational knowledge refinement, entity-action prediction, and dynamic prompt adjustment to improve medical dialogue generation on MedDG and KaMed.
Discussion (0). Sign in to comment.