Summary of Chatqa: Surpassing Gpt-4 on Conversational Qa and Rag, by Zihan Liu et al.
ChatQA: Surpassing GPT-4 on Conversational QA and RAG
by Zihan Liu, Wei Ping, Rajarshi Roy, Peng Xu, Chankyu Lee, Mohammad Shoeybi, Bryan Catanzaro
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces ChatQA, a suite of models that outperforms GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (QA). The authors propose a two-stage instruction tuning method to enhance generation performance, as well as a dense retriever optimized for conversational QA. The ChatQA-1.0-70B model built on Llama2 can slightly outperform GPT-4 models without relying on synthetic data from OpenAI GPT models. Additionally, the authors release open-sourced model weights, instruction tuning data, and the ChatRAG Bench to advance research in this field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new set of computer programs that can understand and answer questions better than other similar programs called GPT-4. The creators came up with two ways to make their program work better: one way helps it generate answers, and the other way helps it find relevant information. They tested their program on many different types of questions and showed that it could do a little better than GPT-4 without using any extra help from OpenAI. |
Keywords
* Artificial intelligence * Gpt * Instruction tuning * Question answering * Rag * Retrieval augmented generation * Synthetic data
