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-augmented generation 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.