Summary of Ango: a Next-level Evaluation Benchmark For Generation-oriented Language Models in Chinese Domain, by Bingchao Wang
ANGO: A Next-Level Evaluation Benchmark For Generation-Oriented Language Models In Chinese Domain
by Bingchao Wang
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses issues with Large Language Models (LLMs) evaluation datasets, proposing a novel Chinese multi-choice question benchmark called ANGO. The authors introduce Keypoint categorization standard, allowing each question to correspond to multiple keypoints and enhancing interpretability of results. A quantifiable question difficulty standard is built based on human performance, dividing questions into 9 levels for more precise model training guidance. The paper also presents exclusive sampling strategies and a new evaluation framework to minimize data leakage impact and leverage ANGO’s innovative features. Experiments demonstrate that ANGO poses a stronger challenge to models and reveals more details in evaluation results compared to existing benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps fix problems with how we test big language models. Right now, many tests are unfair or hard to understand. The authors create a new way to test language models using Chinese questions, called ANGO. This test has special features that make it more accurate and easier to use. It also uses real people’s answers to decide how hard each question is, making it better for training models. To make sure the results are fair, the authors come up with special ways to pick questions and evaluate the models. The tests show that ANGO is a tougher challenge for language models and gives more helpful information. |
