Evaluating Large Language Models (LLMs) is a difficult task, as real-world problems are quite complex and ever-changing. Conventional benchmarks often fail to provide a holistic picture of LLMs’ performance. Here are some key metrics recently highlighted in a LinkedIn post:
1. MixEval: Designed to ensure balance between user queries and effective grading, MixEval combines real-world user inquiries with commercial benchmarks. This method compares web-mined questions with related queries from current benchmarks. An offshoot, MixEval-Hard targets complex queries and offers more opportunities for model improvements.
2. IFEval (Instructional Framework Standardization and Evaluation): Evaluating the capability of LLMs to follow instructions in natural language has been challenging owing to a lack of standard metrics. The IFEval benchmark addresses this by focusing on verifiable instructions. It consists of around 500 prompts with one or more instructions each and offers measurable and comprehensible indicators that facilitate model performance assessment in real-world use cases.
3. Arena-Hard: This automatic evaluation tool for instruction-tuned LLMs consists of 500 complex user queries and uses GPT-4-Turbo as a judge. Arena-Hard-Auto provides a faster and more cost-effective solution compared to its counterpart, Chatbot Arena Category Hard.
4. MMLU (Massive Multitask Language Understanding): MMLU aims to evaluate a model’s multitasking accuracy across various fields, including computer science, law, US history, and basic arithmetic. Given the challenging nature of this task, most models perform at near-random accuracy on this benchmark, suggesting there is room for improvement.
5. GSM8K: This dataset is a collection of 8.5K multilingual mathematical problems aimed at elementary students, created to push the mathematical reasoning capacity of LLMs. Even the most powerful transformer models struggle with this dataset. But by training verifiers to assess the accuracy of model completions, the performance significantly improves.
6. HumanEval: This model assesses Python code-writing skills using Codex, a GPT language model optimized on publicly accessible code from Github. It has proven to outperform models like GPT-3 and GPT-J. HumanEval uses custom programming tasks and unit tests to evaluate code generation models.
These methods and metrics provide a robust framework for evaluating LLMs, and serve as a comprehensive guide for researchers working on model improvement. They also help identify key strengths and weaknesses of LLMs, making them critical tools for driving AI research forward.
