With the rise of Large Language Models (LLMs) and their ubiquitous deployment
in diverse domains, measuring language model behavior on realistic data is
imperative. For example, a company deploying a client-facing chatbot must
ensure that the model will not respond to client requests with profanity.
Current evaluations approach this problem using small, domain-specific datasets
with human-curated labels. These evaluation sets are often sampled from a
narrow and simplified distribution, and data sources can unknowingly be leaked
into the training set which can lead to misleading evaluations. To bypass these
drawbacks, we propose a framework for self-supervised evaluation of LLMs by
analyzing their sensitivity or invariance to transformations on the input text.
Self-supervised evaluation can directly monitor LLM behavior on datasets
collected in the wild or streamed during live model deployment. We demonstrate
self-supervised evaluation strategies for measuring closed-book knowledge,
toxicity, and long-range context dependence, in addition to sensitivity to
grammatical structure and tokenization errors. When comparisons to similar
human-labeled benchmarks are available, we find strong correlations between
self-supervised and human-supervised evaluations. The self-supervised paradigm
complements current evaluation strategies that rely on labeled data.

提出了一种自监督评估框架，用于分析大型语言模型的敏感性或不变性，以测量其中的知识、毒性、远程上下文依赖性等特征，以及语法结构和令牌化错误，这一自监督评估可直接监视大量的真实数据，以帮助评估大型语言模型的行为表现。