Summary of Long-form Factuality in Large Language Models, by Jerry Wei and Chengrun Yang and Xinying Song and Yifeng Lu and Nathan Hu and Jie Huang and Dustin Tran and Daiyi Peng and Ruibo Liu and Da Huang and Cosmo Du and Quoc V. Le
Long-form factuality in large language models
by Jerry Wei, Chengrun Yang, Xinying Song, Yifeng Lu, Nathan Hu, Jie Huang, Dustin Tran, Daiyi Peng, Ruibo Liu, Da Huang, Cosmo Du, Quoc V. Le
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel benchmarking method is proposed to evaluate the long-form factuality of large language models. The authors generate a prompt set, LongFact, comprising thousands of questions spanning 38 topics using GPT-4. A Search-Augmented Factuality Evaluator (SAFE) is introduced, which utilizes an LLM to break down responses into individual facts and evaluates their accuracy through a multi-step reasoning process. Additionally, the F1 score is extended as an aggregated metric for long-form factuality by balancing precision (percentage of supported facts) with recall (percentage of provided facts relative to a user’s preferred response length). This approach aims to improve the evaluation of LLMs’ ability to generate accurate and informative responses in open domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can make mistakes when providing information. To fix this, researchers created a way to test how well these models do at giving correct answers. They made a list of questions on many topics using a powerful model called GPT-4. Then, they came up with a method to check if the answers are accurate by breaking them down into smaller pieces and looking for proof from search engines like Google. This method can help us see how well models do at providing good information. |
Keywords
» Artificial intelligence » F1 score » Gpt » Precision » Prompt » Recall
