Streamlining Model Evaluation with Computation-based Metrics
1. Streamlining Model Evaluation with Computation-based Metrics
With computation-based metrics, you can quickly and effectively measure how well your models are doing by looking at different measurements that are specific to the task you're working on. This method evaluates model results based on the input prompt and output response pairs only. The methodology is in line with academic research and open benchmarks, employing several widely used metrics for various general AI tasks. Vertex AI offers computation-based metrics for both the base and tuned versions of its PaLM text-based LLM. These metrics are tailored to specific tasks with at least one metric available for each core comprehension capability. For classification tasks, for example, you'll find metrics like Micro-F1, Macro-F1, and Per class F1. For summarization, ROUGE-L is provided, which measures how well a summary captures the article's essence by identifying the longest shared word sequence. Consult the supported task section in the Vertex AI documentation to find the most suitable metrics for your specific needs. Additionally, you can run evaluations through the REST API, Vertex AI SDK, or the Google Cloud Console, with detailed instructions available in the official documentation. Using computation-based metrics involves a few simple steps. First, prepare your model evaluation dataset with prompt and ground truth pairs. Next, upload the dataset to a Google Cloud Storage bucket. Finally, submit the model evaluation job using the Vertex AI Python library. When examining the dataset, you have to provide the prompt with the instructions and the context. You'll then need to provide the ground truth, which will be used together with the generated answers to calculate metrics related to the selected task. Include at least 10 examples that closely resemble real-world application scenarios. Once uploaded to Google Cloud Storage, leverage the provided model evaluation pipeline template. Vertex AI model evaluation utilizes Vertex AI Pipelines for scalable model evaluation of GenAI models. Specify parameters like evaluation dataset location, task, output location, and the desired model. Then initiate the model evaluation pipeline job using the Vertex AI Pipeline SDK. Evaluating multiple models may seem straightforward. For example, just run evaluations for each and compare the aggregated metrics. However, these metrics can be difficult to understand. While they can indicate overall model superiority, they don't necessarily reveal which models consistently produce summaries that best align with human preferences. Determining the model that generates the most human-like summaries often requires a more nuanced approach beyond simple metric comparison. While manually examining each pair of summaries to assign a preference score is possible, it is time consuming. To efficiently evaluate LLM alignment at scale, a more automated approach is often necessary.2. Let's practice!
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