Supporting Human-AI Collaboration in Auditing LLMs with LLMs

TL;DR

This paper introduces AdaTest++, an enhanced human-AI collaborative auditing tool for large language models, emphasizing sensemaking and communication to improve failure detection and understanding.

Contribution

It advances LLM auditing by integrating human-AI collaboration principles into AdaTest, demonstrating improved failure detection and reporting capabilities.

Findings

AdaTest++ effectively leverages human strengths in sensemaking and hypothesis testing.

Participants identified diverse failure modes, including under-reported issues.

The tool improves detection of biases and errors in commercial LLMs.

Abstract

Large language models are becoming increasingly pervasive and ubiquitous in society via deployment in sociotechnical systems. Yet these language models, be it for classification or generation, have been shown to be biased and behave irresponsibly, causing harm to people at scale. It is crucial to audit these language models rigorously. Existing auditing tools leverage either or both humans and AI to find failures. In this work, we draw upon literature in human-AI collaboration and sensemaking, and conduct interviews with research experts in safe and fair AI, to build upon the auditing tool: AdaTest (Ribeiro and Lundberg, 2022), which is powered by a generative large language model (LLM). Through the design process we highlight the importance of sensemaking and human-AI communication to leverage complementary strengths of humans and generative models in collaborative auditing. To evaluate the effectiveness of the augmented tool, AdaTest++, we conduct user studies with participants auditing two commercial language models: OpenAI's GPT-3 and Azure's sentiment analysis model. Qualitative analysis shows that AdaTest++ effectively leverages human strengths such as schematization, hypothesis formation and testing. Further, with our tool, participants identified a variety of failures modes, covering 26 different topics over 2 tasks, that have been shown before in formal audits and also those previously under-reported.