Summary of Stackrag Agent: Improving Developer Answers with Retrieval-augmented Generation, by Davit Abrahamyan et al.
StackRAG Agent: Improving Developer Answers with Retrieval-Augmented Generation
by Davit Abrahamyan, Fatemeh H. Fard
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 proposed StackRAG system aims to combine the strengths of Large Language Models (LLMs) and the Stack Overflow (SO) platform to generate reliable and accurate answers to developer questions. By leveraging LLMs and SO’s knowledge repository, StackRAG retrieves relevant information from SO and uses it to augment its generated answers. This approach tackles the limitations of relying solely on LLMs for answer generation, which can lead to irrelevant or unreliable results (hallucination). The system shows promise in generating correct, accurate, relevant, and useful responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary StackRAG is a new way to get help when you’re searching for answers online. Right now, we have two main tools: Stack Overflow and Large Language Models like ChatGPT. Both are helpful, but they each have their own problems. Searching on Stack Overflow can take a long time, while using LLMs can give you bad or fake information. The idea behind StackRAG is to combine the best of both worlds. It uses LLMs to generate answers and then adds extra information from Stack Overflow to make them more reliable. The first tests show that this approach works well, giving us accurate and useful responses. |
Keywords
* Artificial intelligence * Hallucination
