1. The rapid pace of AI model development makes it impossible to comprehensively evaluate models like ChatGPT or Gemini due to their broad and opaque nature.
2. Synthetic benchmarks provide only a limited view of certain capabilities of AI models, and companies like Google and OpenAI rely on this lack of comprehensive evaluation for consumer trust.
3. Despite the challenges, it is important to attempt to review AI models to provide a real-world counterbalance to industry hype. Our approach involves subjective testing to assess the qualitative differences in performance, focusing on real-world use cases and providing an honest assessment of the model’s capabilities.
The rapid pace of AI model release has made it nearly impossible to comprehensively evaluate systems like ChatGPT or Gemini. Large models like these are constantly updated and contain multiple components, making it challenging to assess their capabilities. Companies like Google and OpenAI benefit from this lack of comprehensive evaluation, leading consumers to rely on their claims.
Even though reviews of AI models may be limited and inconsistent, qualitative analysis provides a real-world counterweight to industry hype. Synthetic benchmarks offer only abstract views of specific capabilities, and companies often target these metrics to showcase their performance. As a result, benchmarks are not sufficient for evaluating models overall.
Testing AI models involves asking a variety of questions to assess their capabilities, such as handling evolving news stories, providing trivia answers, and offering medical or mental health advice. By testing models in common scenarios and interactions, reviewers can provide a subjective judgment of their performance. These evaluations aim to go beyond quantitative benchmark scores and provide a more nuanced understanding of each model’s strengths and weaknesses.
While it is impossible to review AI models comprehensively, ongoing testing and evaluation can offer valuable insights into their practical use cases and limitations. Reviewers focus on real-world scenarios and experiences to provide a holistic view of each model’s performance, taking into consideration factors that automated benchmarks may overlook. By continually refining their evaluation methods, reviewers aim to offer valuable assessments of AI models that go beyond standardized benchmarks.