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

Neel Jain, Khalid Saifullah, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

Keywords: evaluation, LLMs

TL;DR: A new way to evaluate LLMs through invariances/sensitivities.

Abstract: 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 alleviate the issues in traditional evaluation, we propose a complementary 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, 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.

Primary Area: generative models

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Submission Number: 8000