Summary of Scopeqa: a Framework For Generating Out-of-scope Questions For Rag, by Zhiyuan Peng and Jinming Nian and Alexandre Evfimievski and Yi Fang
ScopeQA: A Framework for Generating Out-of-Scope Questions for RAG
by Zhiyuan Peng, Jinming Nian, Alexandre Evfimievski, Yi Fang
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 The paper presents a novel method for generating high-quality training data for conversational AI agents that use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses. The approach focuses on creating diverse sets of borderline out-of-scope confusing questions for a given document corpus, which is essential for improving the accuracy of RAG agents in detecting and responding to ambiguous or false assumptions. The authors evaluate several large language models as RAG agents and compare their performance in terms of confusion detection and response generation. The study contributes a benchmark dataset to the public domain, which can be used to further develop and improve RAG-based conversational AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us create better chatbots that answer questions correctly. Chatbots often struggle with tricky questions that are based on false assumptions or are hard to understand. To make them smarter, we need more training data that includes these kinds of questions. The authors developed a new way to generate this kind of data and tested several large language models to see which one works best at answering confusing questions. They also shared their dataset with the public so others can use it to improve chatbot technology. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
