Summary of Rac: Efficient Llm Factuality Correction with Retrieval Augmentation, by Changmao Li and Jeffrey Flanigan
RAC: Efficient LLM Factuality Correction with Retrieval Augmentation
by Changmao Li, Jeffrey Flanigan
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to enhancing the factual performance of Large Language Models (LLMs) is presented, without requiring additional fine-tuning. The Retrieval Augmented Correction (RAC) method decomposes LLM output into atomic facts and applies a verification and correction process using retrieved content. This technique improves upon state-of-the-art baselines by up to 30% on two popular factuality evaluation datasets, demonstrating its efficacy and robustness across different LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make Large Language Models more accurate is introduced. It’s called Retrieval Augmented Correction (RAC). This method checks the facts in what the model says and makes sure they’re correct. RAC works with any instruction-tuned Large Language Model, and it does this without making the model learn anything new. This approach really helps improve the accuracy of the model’s outputs. |
Keywords
» Artificial intelligence » Fine tuning » Large language model
