ChatQA: Building GPT-4 Level Conversational QA Models
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.
65a9dc6e2ed95c799fe0279d
Zihan Liu
claimed_verified
2024-09-19T07:08:52.492Z
65f33b1c9f7970ccc0234cbf
/avatars/99fbab303912e3674663251c04279907.svg
Zihan Liu
user
zihanliu
65a9dc6e2ed95c799fe0279e
Wei Ping
claimed_verified
2024-04-15T08:01:01.484Z
661cdb08ed360bdc4f6db37c
/avatars/bced64e9b7f46125a03d6f6e66ae7e33.svg
Wei Ping
user
wpingnet
65a9dc6e2ed95c799fe0279f
Rajarshi Roy
65a9dc6e2ed95c799fe027a0
Peng Xu
65a9dc6e2ed95c799fe027a1
Mohammad Shoeybi
claimed_verified
2024-05-28T07:20:00.309Z
6641544c695975af2cbd0da6
/avatars/0ad3c18dcba585259b064fe9b00a07ce.svg
Mohammad Shoeybi
user
shoeybi
65a9dc6e2ed95c799fe027a2
Bryan Catanzaro
admin_assigned
2024-01-19T08:17:21.011Z
6311021788942700629e6247
/avatars/e7adc1632b76e80e7e4a590033d1c20a.svg
Bryan Catanzaro
user
ctnzr
ChatQA: Building GPT-4 Level Conversational QA Models
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.
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.
ChatQA conversational QA two-stage instruction tuning dense retriever multi-turn QA dataset zero-shot conversational QA large language models (LLMs) synthetic data