The benchmarks for evaluating models provide insights into the capabilities of each model and allow for effective comparison to help users of determine where and how to fine-tune the models and with what additional data to enable practical deployment.
Generative AI models have evolved significantly over the years, making it increasingly difficult to assess and determine which is best. It has become essential to establish reliable evaluation frameworks that can accurately judge the quality of models.
Moreover, a proper framework will help authorities and concerned agencies to assess the safety, accuracy, reliability, or usability issues of a model.
None of the frameworks are sufficient on their own nor do they consider safety as a factor for evaluation.
Below are some of the important factors that should be present within a framework:
Different frameworks utilize various prompting approaches and may provide independent scores for each respective prompt:
Generally speaking, multi-shot approaches yield higher benchmark results than zero shot approaches.
Due to the large number of large language models and the growing number of benchmarking frameworks, EleutherAI developed a “harness” that allows developers to automate the benchmarking of the LLM models using a few-shot approach.
Hugging Face maintains an LLM leaderboard that tracks the benchmark performance of the latest open-source models across numerous frameworks.
Stanford University developed HELM (Holistic Evaluation of Language Models), a comprehensive framework for evaluating foundation models. A leaderboard tracks the performance of the evaluated models.
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