Bring Your Own Data! Self-Sensitivity Evaluation for Large Language Models

摘要

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. To alleviate these issues in traditional evaluation, we propose a complementary framework for additional self-sensitivity evaluation of LLMs by analyzing their sensitivity or invariance to transformations on the input text. Self-sensitivity evaluation can directly monitor LLM behavior on datasets collected in-the-wild or streamed during live model deployment. We demonstrate self-sensitivity evaluation strategies for measuring closed-book knowledge, toxicity, 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-sensitivity and human-supervised evaluations. The self-sensitivity paradigm complements current evaluation strategies that rely on labeled data.