Summary of Ra-isf: Learning to Answer and Understand From Retrieval Augmentation Via Iterative Self-feedback, by Yanming Liu et al.
RA-ISF: Learning to Answer and Understand from Retrieval Augmentation via Iterative Self-Feedback
by Yanming Liu, Xinyue Peng, Xuhong Zhang, Weihao Liu, Jianwei Yin, Jiannan Cao, Tianyu Du
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Retrieval Augmented Iterative Self-Feedback (RA-ISF), a framework to enhance the problem-solving capabilities of large language models (LLMs). By integrating external knowledge through retrieval-augmented generation (RAG) methods, RA-ISF iteratively decomposes tasks into three submodules. This approach improves performance in certain scenarios and outperforms existing benchmarks on models like GPT3.5 and Llama2, enhancing factual reasoning capabilities while reducing hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better by combining what they already know with new information from the internet. It’s like a smart way to solve problems that humans can do too! By breaking down tasks into smaller parts and using this new information, the computer gets smarter and makes fewer mistakes. This is important because it can help us use computers for things like writing stories or giving advice. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
