Summary of Boosting Conversational Question Answering with Fine-grained Retrieval-augmentation and Self-check, by Linhao Ye et al.
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
by Linhao Ye, Zhikai Lei, Jianghao Yin, Qin Chen, Jie Zhou, Liang He
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposes a novel approach to retrieval-augmented generation (RAG) for conversational question answering (CQA). By fine-tuning large language models with external knowledge and incorporating self-check mechanisms, the authors aim to generate more accurate responses. The proposed RAG architecture consists of three components: conversational question refiner, fine-grained retriever, and self-check based response generator. Experimental results demonstrate significant improvements over state-of-the-art baselines on a newly released Chinese CQA dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to answer questions in conversations. Right now, most AI systems can only answer one question at a time. But what if we could teach them to understand the context of the conversation and give more accurate answers? The researchers propose a new way to do this called Retrieval-Augmented Generation (RAG). It works by combining large language models with external knowledge and checking its own responses to make sure they are correct. They test their approach on a big dataset of Chinese conversations and show that it outperforms other methods. |
Keywords
» Artificial intelligence » Fine tuning » Question answering » Rag » Retrieval augmented generation
