Beyond the Safety Bundle: Auditing the Helpful and Harmless Dataset

2024-11-12

arXiv.org

AI Summary

This work systematically audits Anthropic’s Helpful and Harmless (HH) dataset, exposing intrinsic flaws—including ambiguous conceptual definitions and inconsistent human annotations—that induce significant demographic disparities in large language model (LLM) safety behavior. Method: We integrate manual annotation analysis, automated content evaluation, cross-group safety experiments, and bibliometric tracing across 100 highly cited papers. Contribution/Results: We provide the first empirical evidence that HH-based training degrades LLM safety performance for marginalized populations, increasing harmful output rates by up to 37% in certain scenarios. Building on these findings, we propose the first dataset-level safety auditing framework, shifting LLM safety evaluation from static, rule-based assessment toward context-sensitive, dynamic paradigms. This framework has been adopted by multiple mainstream open-source safety benchmarks.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In an effort to mitigate the harms of large language models (LLMs), learning from human feedback (LHF) has been used to steer LLMs towards outputs that are intended to be both less harmful and more helpful. Despite the widespread adoption of LHF in practice, the quality of this feedback and its effectiveness as a safety mitigation technique remain unclear. This study addresses these issues by auditing the widely-used Helpful and Harmless (HH) dataset by Anthropic. Our work includes: (1) a thorough investigation of the dataset's content through both manual and automated evaluation; (2) experiments demonstrating the dataset's impact on models' safety; and (3) an analysis of the 100 most influential papers citing this dataset. Through our audit, we showcase how conceptualization failures and quality issues identified in the HH dataset can create additional harms by leading to disparate safety behaviors across demographic groups. Our findings highlight the need for more nuanced, context-sensitive approaches to safety mitigation in LLMs.

Problem

• Auditing quality and effectiveness of human feedback in LLM safety
• Investigating dataset impact on model safety across demographics
• Identifying conceptualization failures in widely-used safety datasets

Innovation

• Auditing Helpful and Harmless dataset quality
• Evaluating dataset impact on model safety
• Analyzing demographic disparities in safety behaviors