title: ChatQA: Building GPT-4 Level Conversational QA Models
summary: 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.
paper_authors._id: 65a9dc6e2ed95c799fe0279d
paper_authors.name: Zihan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-19T07:08:52.492Z
paper_authors._id: 65f33b1c9f7970ccc0234cbf
paper_authors.avatarUrl: /avatars/99fbab303912e3674663251c04279907.svg
paper_authors.fullname: Zihan Liu
paper_authors.type: user
paper_authors.user: zihanliu
paper_authors._id: 65a9dc6e2ed95c799fe0279e
paper_authors.name: Wei Ping
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-15T08:01:01.484Z
paper_authors._id: 661cdb08ed360bdc4f6db37c
paper_authors.avatarUrl: /avatars/bced64e9b7f46125a03d6f6e66ae7e33.svg
paper_authors.fullname: Wei Ping
paper_authors.type: user
paper_authors.user: wpingnet
paper_authors._id: 65a9dc6e2ed95c799fe0279f
paper_authors.name: Rajarshi Roy
paper_authors._id: 65a9dc6e2ed95c799fe027a0
paper_authors.name: Peng Xu
paper_authors._id: 65a9dc6e2ed95c799fe027a1
paper_authors.name: Mohammad Shoeybi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-28T07:20:00.309Z
paper_authors._id: 6641544c695975af2cbd0da6
paper_authors.avatarUrl: /avatars/0ad3c18dcba585259b064fe9b00a07ce.svg
paper_authors.fullname: Mohammad Shoeybi
paper_authors.type: user
paper_authors.user: shoeybi
paper_authors._id: 65a9dc6e2ed95c799fe027a2
paper_authors.name: Bryan Catanzaro
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-19T08:17:21.011Z
paper_authors._id: 6311021788942700629e6247
paper_authors.avatarUrl: /avatars/e7adc1632b76e80e7e4a590033d1c20a.svg
paper_authors.fullname: Bryan Catanzaro
paper_authors.type: user
paper_authors.user: ctnzr
paper_title: ChatQA: Building GPT-4 Level Conversational QA Models
paper_summary: 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.
paper_ai_summary: ChatQA uses a two-stage instruction tuning method with a dense retriever to improve zero-shot conversational QA performance, outperforming GPT-4 on multiple datasets.
paper_ai_keywords: ChatQA conversational QA two-stage instruction tuning dense retriever multi-turn QA dataset zero-shot conversational QA large language models (LLMs) synthetic data