What is the best model? Application-driven Evaluation for Large Language Models
This work addresses the challenge for users in choosing cost-effective LLMs for real-world tasks, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of selecting the best large language model for practical applications by introducing A-Eval, an application-driven evaluation benchmark, which categorizes tasks, constructs a dataset of 678 Q&A pairs, and evaluates models to reveal scaling laws and provide selection guidance.
General large language models enhanced with supervised fine-tuning and reinforcement learning from human feedback are increasingly popular in academia and industry as they generalize foundation models to various practical tasks in a prompt manner. To assist users in selecting the best model in practical application scenarios, i.e., choosing the model that meets the application requirements while minimizing cost, we introduce A-Eval, an application-driven LLMs evaluation benchmark for general large language models. First, we categorize evaluation tasks into five main categories and 27 sub-categories from a practical application perspective. Next, we construct a dataset comprising 678 question-and-answer pairs through a process of collecting, annotating, and reviewing. Then, we design an objective and effective evaluation method and evaluate a series of LLMs of different scales on A-Eval. Finally, we reveal interesting laws regarding model scale and task difficulty level and propose a feasible method for selecting the best model. Through A-Eval, we provide clear empirical and engineer guidance for selecting the best model, reducing barriers to selecting and using LLMs and promoting their application and development. Our benchmark is publicly available at https://github.com/UnicomAI/DataSet/tree/main/TestData/GeneralAbility.