Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model’s ability to understand and generate language in a manner similar to humans. Most of these works focus on proposing new datasets, however, we see two main issues within previous benchmarking pipelines, namely testing leakage and evaluation automation. In this paper, we propose a novel benchmarking framework, Language-Model-as-an-Examiner, where the LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner. Our framework allows for effortless extensibility as various LMs can be adopted as the examiner, and the questions can be constantly updated given more diverse trigger topics. For a more comprehensive and equitable evaluation, we devise three strategies: (1) We instruct the LM examiner to generate questions across a multitude of domains to probe for a broad acquisition, and raise follow-up questions to engage in a more in-depth assessment. (2) Upon evaluation, the examiner combines both scoring and ranking measurements, providing a reliable result as it aligns closely with human annotations. (3) We additionally propose a decentralized Peer-examination method to address the biases in a single examiner. Our data and benchmarking results are available at: http://lmexam.xlore.cn.
Discipline
Databases and Information Systems | Programming Languages and Compilers
Research Areas
Data Science and Engineering
Publication
Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14
Publisher
Neural information processing systems foundation
City or Country
California
Citation
BAI, Yushi; YING, Jiahao; CAO, Yixin; LV, Xin; HE, Yuze; WANG, Xiaozhi; YU, Jifan; ZENG, Kaisheng; XIAO, Yijia; LYU, Haozhe; ZHANG, Jiayin; LI, Juanzi; and HOU, Lei.
Benchmarking foundation models with language-model-as-an-examiner. (2023). Proceedings of the 37th Conference on Neural Information Processing, New Orleans, United States, December 12-14.
Available at: https://ink.library.smu.edu.sg/sis_research/8392
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.