Task-specific metrics

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Different LLM tasks require tailored assessment metrics.

**For dialog systems, metrics could assess user engagement or task completion rates. In the case of code generation, successful intent could be measured by how often the generated code compiles or passes tests.

Example: In a customer support chatbot, engagement levels could be measured by how long users stay in a conversation or by the number of follow-up questions they ask.

If users frequently request additional information, this indicates that the model is thailand whatsapp number data successfully capturing their attention and effectively resolving their queries.

8. Robustness and equity
Assessing a model’s robustness involves checking how well it responds to unexpected or unusual inputs. Fairness metrics help identify biases in model outputs, ensuring equitable performance across different demographics and scenarios.

Example: When a model is tested with a whimsical question such as "What do you think about unicorns?", the model should respond to the question gracefully and give a relevant answer. If it instead gives a nonsensical or inappropriate answer, this indicates a lack of robustness.

Fairness testing ensures that the model does not produce biased or harmful results, promoting a more inclusive AI system .

Read more: The difference between machine learning and artificial intelligence

9. Efficiency metrics
As the complexity of language models increases, it becomes increasingly important to measure their efficiency in terms of speed, memory usage, and power consumption. Efficiency metrics help evaluate a model's resource consumption in generating answers.

Example: For a large language model, measuring efficiency might involve tracking how quickly it generates responses to user queries and how much memory it uses in the process.

If it takes too long to respond or consumes too many resources, it could be a problem for applications that require real-time performance, such as chatbots or translation services.

Now you know how to evaluate an LLM model. But what tools can you use to measure it? Let’s explore.