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Numerous benchmarks have been established to assess the performance offoundation models on open-ended question answering, which serves as acomprehensive test of a model's ability to understand and generate language ina manner similar to humans. Most of these works focus on proposing newdatasets, however, we see two main issues within previous benchmarkingpipelines, namely testing leakage and evaluation automation. In this paper, wepropose a novel benchmarking framework, Language-Model-as-an-Examiner, wherethe LM serves as a knowledgeable examiner that formulates questions based onits knowledge and evaluates responses in a reference-free manner. Our frameworkallows for effortless extensibility as various LMs can be adopted as theexaminer, and the questions can be constantly updated given more diversetrigger topics. For a more comprehensive and equitable evaluation, we devisethree strategies: (1) We instruct the LM examiner to generate questions acrossa multitude of domains to probe for a broad acquisition, and raise follow-upquestions to engage in a more in-depth assessment. (2) Upon evaluation, theexaminer combines both scoring and ranking measurements, providing a reliableresult as it aligns closely with human annotations. (3) We additionally proposea decentralized Peer-examination method to address the biases in a singleexaminer. Our data and benchmarking results are available at:http://lmexam.xlore.cn.

Benchmarking Foundation Models with Language-Model-as-an-Examiner