Skip to content

Crafting Custom Metrics for Measuring Performance of Finetuned Large Language Models

In this blog post, we will discuss the importance of defining custom metrics for measuring the performance of finetuned large language models.

Our no-code LLMOps platform for finetuning LLM models - EasyFinetuner, has built-in metrics and ability to add your own custom metrics for evaluating finetuned LLMs. We will also delve into how different problems like classification, logical reasoning, and generative AI may require different metrics and how to design a custom metric tailored to each problem.

Introduction

As artificial intelligence and machine learning continue to advance rapidly, large language models such as OpenAI's GPT-3 have demonstrated remarkable capabilities in natural language understanding and generation. These models have proven effective in a wide variety of applications, including text classification, logical reasoning, and generative AI. However, to refine these models and optimize their performance, we need to measure their effectiveness accurately. This is where custom metrics come into play.

Why Custom Metrics?

Large language models are often evaluated using general metrics like training loss and validation loss. While these metrics provide valuable insights into the model's overall performance, they may not be relevent for many problems. Custom metrics, on the other hand, allow us to design evaluation measures that cater to the unique requirements of each problem. This enables a more accurate assessment of a model's performance, guiding the fine-tuning process and ultimately leading to better results.

Classification Problems

In classification tasks, we aim to assign an input to one of several predefined categories. Standard metrics like accuracy and F1 score are commonly used to evaluate performance in these cases. However, depending on the problem, we might need to prioritize certain aspects of the model's predictions.

For instance, in a sentiment analysis task where we classify texts as positive, negative, or neutral, we might be more concerned with the model's ability to correctly identify extremely positive or negative statements. In this case, we could design a custom metric that assigns a higher weight to extreme sentiment predictions, allowing us to fine-tune the model to prioritize this aspect.

Logical Reasoning

Logical reasoning tasks often involve evaluating the model's ability to understand and infer relationships between different entities and concepts. Standard metrics may not be sufficient to capture the complexity of these tasks. In such cases, custom metrics can help quantify how well the model performs in specific aspects of logical reasoning.

For example, consider a task where the model is expected to answer questions based on a set of given facts. We could design a custom metric that evaluates the model's performance based on factors such as:

  1. The correctness of the answer
  2. The level of reasoning complexity required to arrive at the answer

By measuring the model's performance across these dimensions, we can obtain a more accurate understanding of its logical reasoning capabilities.

Generative AI

Generative AI tasks involve the creation of new text. Evaluating the performance of models in these tasks can be particularly challenging due to the subjective nature of the generated content.

In such cases, custom metrics can be designed to assess the quality of the generated content based on specific criteria. For example, in a text generation task, we could design a metric that takes into account factors such as:

  1. Grammatical correctness
  2. Semantic coherence
  3. Creativity or novelty of the generated content
  4. Relevance to the given prompt or context

By incorporating these factors into a custom metric, we can better assess the performance of the generative AI model and fine-tune it to generate higher-quality content.

Designing Custom Metrics

When designing custom metrics for measuring the performance of finetuned large language models, it's essential to keep the following considerations in mind:

  1. Identify the specific aspects of the model's performance that are most relevant to the task at hand.
  2. Ensure that the custom metric is computationally feasible and can be calculated efficiently.
  3. Validate the custom metric using domain expertise and, if possible, human evaluation to ensure it captures the desired aspects of the model's performance.

Conclusion

In conclusion, crafting custom metrics that are calculatable using a specific logic is crucial for accurately measuring the performance of finetuned large language models. By designing evaluation measures tailored to each problem, we can better understand the model's strengths and weaknesses, optimize its performance, and ultimately unlock the full potential of these powerful AI tools.

Comments