ChatQA: Building GPT-4 Level Conversational QA Models
Authors: name: Zihan Liu, name: Wei Ping, name: Rajarshi Roy, name: Peng Xu, name: Mohammad Shoeybi, name: Bryan Catanzaro

In this work, we introduce ChatQA, a family of conversational question
answering (QA) models, that obtain GPT-4 level accuracies. Specifically, we
propose a two-stage instruction tuning method that can significantly improve
the zero-shot conversational QA results from large language models (LLMs). To
handle retrieval in conversational QA, we fine-tune a dense retriever on a
multi-turn QA dataset, which provides comparable results to using the
state-of-the-art query rewriting model while largely reducing deployment cost.
Notably, our ChatQA-70B can outperform GPT-4 in terms of average score on 10
conversational QA datasets (54.14 vs. 53.90), without relying on any synthetic
data from OpenAI GPT models.