Summary of Measuring and Reducing Llm Hallucination Without Gold-standard Answers, by Jiaheng Wei et al.
Measuring and Reducing LLM Hallucination without Gold-Standard Answers
by Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo, Andrew Estornell, Yang Liu
First submitted to arxiv on: 16 Feb 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 This paper tackles the critical issue of LLM (Large Language Model) hallucination, where models produce factually incorrect yet convincing answers. To combat this problem, researchers need to develop a reliable metric for measuring hallucination. However, existing metrics require expensive and error-prone human-written gold-standard answers. The authors propose FEWL (Factualness Evaluations via Weighting LLMs), a novel approach that leverages off-the-shelf LLMs as proxies for gold-standard answers. They demonstrate theoretical guarantees and empirical accuracy of FEWL, showcasing its potential to reduce hallucination through in-context learning and supervised fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure big language models don’t make up fake answers. These models can sometimes give wrong information that sounds right. To stop this from happening, we need a way to measure how often it happens. Right now, we use special human-written answers to do this, but that’s expensive and might not be perfect. The authors came up with a new idea called FEWL (Factualness Evaluations via Weighting LLMs) that uses other language models as a reference instead. They showed that this works better than the old way and can even help reduce fake answers by training the model in different ways. |
Keywords
* Artificial intelligence * Fine tuning * Hallucination * Large language model * Supervised
