Summary of Instructrag: Instructing Retrieval-augmented Generation Via Self-synthesized Rationales, by Zhepei Wei et al.
InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales
by Zhepei Wei, Wei-Lin Chen, Yu Meng
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Retrieval-augmented generation (RAG) has shown promise for improving language model accuracy. However, noisy inputs can introduce errors into the retrieved contents. Existing RAG methods directly predict answers without explicitly addressing this issue, relying on implicit denoising processes. To overcome this challenge, we propose InstructRAG, where language models learn explicit denoising through self-synthesized rationales. These rationales are derived from ground-truth answers and can be used for in-context learning or supervised fine-tuning. Compared to standard RAG approaches, InstructRAG requires no additional supervision, improves generation accuracy, and allows for easier verification of predicted answers. Experiments show that InstructRAG consistently outperforms existing methods across five knowledge-intensive benchmarks, achieving a relative improvement of 8.3%. The method also scales well with increased retrieved documents and exhibits robust denoising ability in out-of-domain datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine if you could use language models to answer questions more accurately. One problem is that the information used to train these models can sometimes be wrong or misleading. To fix this, we created a new method called InstructRAG. This method helps language models learn how to correct mistakes and provide better answers by having them explain why they think something is true. We tested our approach on five different tests and found that it worked much better than other methods. Our approach also gets better as more information is added, and it can handle situations where the information is different from what we’ve seen before. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Rag » Retrieval augmented generation » Supervised
