In this video we explore the various metrics, benchmarks, and techniques available to evaluate Large Language Models such as GPT-4, Llama 2, or Falcon for a particular use case.
Evaluating language models for specific use cases is a challenging problem in the language modeling space. Unlike in other areas of machine learning, it is not straightforward to evaluate language models for a particular use case. While there are metrics and benchmarks, they mostly apply to generic tasks, and there is no one-size-fits-all process to evaluate the performance of a model for a specific use case.
Model evaluation is the statistical measurement of the performance of a machine learning model on a particular use case, measured on a large dataset independent from the training dataset. It is a crucial part of model development, and all ML teams dedicate significant resources to establish rigorous evaluation procedures. Setting up a solid evaluation process as part of the development workflow is essential to guarantee performance and safety.
Evaluating language models is challenging due to the unstructured nature of text generated by these models. Unlike other types of models that return structured outputs, language models generate unstructured text, making it difficult to define correctness. There are several metrics available to score language models, such as BLEU and ROUGE, which are commonly used for translation and summarization tasks. However, these metrics may not be suitable indicators of how a model will perform on a specific task, as they do not account for intelligibility or grammatical correctness.
Various benchmarks and leaderboards, such as GLUE and HellaSwag, rank different language models based on their performance on specific tasks. While these benchmarks are useful for comparing language models, they may not provide insights into how the models will perform for a particular task on specific input data.
Each application needs to develop its own evaluation procedure, which can be a significant amount of work. One approach to address this challenge is to use another model to grade the output of a language model. By describing the task and grading criteria to another model, specialized metrics can be crafted for specific applications.
RAG involves building a system around the model, including a query engine and a vector database, to enable the model to respond to questions with data it has not seen before. Fine-tuning, on the other hand, requires training pipelines and skills to fine-tune the model with specific data. Both RAG and fine-tuning are complex techniques that require significant effort and resources.
Evaluating language models for specific tasks requires careful consideration of the metrics, benchmarks, and custom evaluation procedures. While existing metrics and benchmarks provide valuable insights, they may not fully capture the performance of language models for specific use cases. Crafting specialized metrics and leveraging advanced techniques like RAG and fine-tuning can help address the challenges of evaluating language models for specific applications.
Airtrain offers evaluation and fine-tuning as a platform, providing users with the ability to upload datasets, select models for comparison, describe the properties to measure, and visualize metric distribution across the entire dataset. This platform aims to enable data-driven decision-making about the choice of language models.In conclusion, evaluating language models for specific tasks is a complex and multifaceted process that requires careful consideration of various factors, including metrics, benchmarks, and custom evaluation procedures. Leveraging specialized metrics and advanced techniques can help address the challenges of evaluating language models for specific applications.
