RELEVANCE:
Generative AI (GenAI) evaluation framework designed to automatically evaluate creative responses from Large Language Models (LLMs)
| Use Case | Description |
| Comprehensive Feedback | The combination allows evaluators to not only measure how “correct” or “appropriate” an individual response is but also to analyze the overall behavior of the AI across a series of responses. This is crucial for applications like chatbots, creative content generation, and educational tools, where context and progression are important. |
| Improved Model Training | These metrics can also inform the training process for AI models. Understanding where and how frequently inversions occur or how long the longest increasing subsequence is can help in tuning the model to produce more coherent and contextually appropriate responses. |
| Scalability | Automatic evaluations are scalable and can handle large volumes of data without the need for extensive human intervention. This is especially useful in iterative development environments and continuous deployment settings. |
| Objective Analysis | By quantifying aspects of the generated content, developers and researchers can more objectively compare different models or different configurations of the same model, leading to more data-driven decision-making. |
| Detecting Anomalies | Each metric’s sensitivity to changes in LLM behavior is crucial for ongoing monitoring. Permutation Entropy, for example, is highly sensitive to minor changes in response diversity, making it an excellent early warning system for detecting drift. Count Inversions and LIS, while slightly less sensitive to minor fluctuations, provide robust indicators of more significant shifts in model behavior that affect response quality and ordering. By examining metrics like CIN and LIS, you can effectively detect and mitigate potential issues like hallucinations or inconsistencies in the AI’s response generation, which might not be evident through relevance scoring alone. |