Research Summary: HarmReduction: Benchmarking LLMs in Harm Reduction Information Provision to Support People Who Use Drugs

A new research benchmark, 'HarmReduction', has been introduced to evaluate the accuracy and safety risks of Large Language Models (LLMs) in providing harm reduction information. This initiative addresses the potential of LLMs to support individuals who use drugs by offering non-judgmental, evidence-based information, a critical public health strategy to improve health outcomes and reduce safety risks. The benchmark dataset, HR-Basic, comprises 2,160 question-answer-evidence pairs covering three specific tasks, aiming to systematically assess LLM performance in this sensitive domain.

Why it matters

This research highlights the critical need to evaluate emerging technological capabilities, specifically LLMs, in sensitive public health contexts. Ensuring the accurate and safe provision of harm reduction information via AI is paramount for improving health outcomes and mitigating risks for vulnerable populations. It sets a foundation for responsible AI deployment in areas requiring high fidelity and ethical considerations.

Key insights

• Millions of individuals face health challenges due to substance use.
• Harm reduction is a public health strategy focused on providing non-judgmental, evidence-based information.
• The strategy aims to improve health outcomes and reduce safety risks for people who use drugs.
• Large Language Models (LLMs) show potential for medical reasoning, suggesting they could address information needs in this area.
• Current LLM performance in harm reduction information provision remains largely unexplored.
• The 'HarmReduction' benchmark and its HR-Basic dataset (2,160 QAE pairs) have been developed to evaluate LLM accuracy and safety in this context.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2507.21815