Summary of What Is the Best Model? Application-driven Evaluation For Large Language Models, by Shiguo Lian et al.
What is the best model? Application-driven Evaluation for Large Language Models
by Shiguo Lian, Kaikai Zhao, Xinhui Liu, Xuejiao Lei, Bikun Yang, Wenjing Zhang, Kai Wang, Zhaoxiang Liu
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces A-Eval, a benchmark for evaluating general large language models (LLMs) in practical application scenarios. It categorizes evaluation tasks into five main categories and 27 sub-categories from a practical perspective, and constructs a dataset of 678 question-and-answer pairs. The authors design an objective evaluation method and evaluate various LLMs of different scales on A-Eval. They reveal interesting laws regarding model scale and task difficulty level, and propose a feasible method for selecting the best model. The benchmark is publicly available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it easier to choose the right language model by creating a special test called A-Eval. It groups tasks into categories like chatbots or summarization, and collects 678 questions and answers as examples. The researchers designed a way to measure how well different models do on these tasks, and found some surprising patterns about how big the model is and how hard the task is. They also give tips on how to pick the best model for your needs. |
Keywords
» Artificial intelligence » Language model » Summarization
