Summary of How to Choose a Threshold For An Evaluation Metric For Large Language Models, by Bhaskarjit Sarmah et al.
How to Choose a Threshold for an Evaluation Metric for Large Language Models
by Bhaskarjit Sarmah, Mingshu Li, Jingrao Lyu, Sebastian Frank, Nathalia Castellanos, Stefano Pasquali, Dhagash Mehta
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG); Statistical Finance (q-fin.ST); Applications (stat.AP)
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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 methodology is proposed to identify a robust threshold for large language models (LLMs) evaluation metrics, addressing the crucial need for reliable monitoring and deployment of these AI systems. By translating traditional model risk management guidelines from regulated industries like finance, the approach starts by identifying risks and stakeholder tolerance, then employs statistically rigorous procedures using available ground-truth data to determine a threshold for a given LLM evaluation metric. As a concrete example, the Faithfulness metric is used with the HaluBench dataset, demonstrating the proposed methodology’s effectiveness. This work lays the foundation for systematic approaches to select thresholds not only for LLMs but also for other General AI applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are super smart computers that can understand and generate human-like text. But how do we make sure they’re working correctly? To answer this question, researchers have developed special tools to test these models. However, there’s a big problem – nobody knows what the “right” score is for these tests! This paper proposes a step-by-step plan to figure out what the perfect score is for any given LLM. The plan starts by thinking about the risks and concerns of using these AI systems, then uses real data to determine a good score. As an example, this method was used with a specific type of test called Faithfulness, and it worked great! This research will help us make better decisions when using these powerful AI models. |
