Summary of Tell Me the Truth: a System to Measure the Trustworthiness Of Large Language Models, by Carlo Lipizzi
Tell me the truth: A system to measure the trustworthiness of Large Language Models
by Carlo Lipizzi
First submitted to arxiv on: 8 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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 addresses the pressing issue of limited confidence in the trustworthiness of Large Language Models (LLMs), such as ChatGPT-4, which demonstrated an 80.1% false-positive error rate in identifying usability issues on websites. The authors propose a systematic approach to measure the trustworthiness of LLMs based on a predefined ground truth represented as a knowledge graph of the domain. This process involves humans in the loop to validate the representation and fine-tune the system. The paper provides a framework for evaluating the trustworthiness of LLMs, which is crucial for their adoption in various domains, including pediatric medical cases where accuracy rates were found to be low (17% according to JAMA Pediatrics). The proposed approach aims to improve the reliability of LLM-based decision-making and foster more widespread acceptance of these powerful AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how we can trust computers that can understand and generate human language, like ChatGPT. Right now, many people are worried that these computers might not always be accurate or reliable. The authors want to find a way to measure how trustworthy these computers are, so they can be used in important situations. They came up with an idea called a “knowledge graph” that represents the information we already know about a certain topic. Then, humans and computers work together to make sure this representation is accurate and helpful. The goal is to make it easier for us to trust these computers when we need them. |
Keywords
» Artificial intelligence » Knowledge graph
