Evaluating Proactive Risk Awareness of Large Language Models
This addresses a critical gap in LLM safety for real-world ecological responsibility, though it is incremental as it focuses on evaluation rather than new mitigation methods.
The paper tackles the problem of evaluating whether large language models (LLMs) can anticipate unintended ecological risks before harm occurs, by introducing a proactive risk awareness framework and the Butterfly dataset with 1,094 queries, finding significant declines in awareness under length restrictions and persistent blind spots in species protection.
As large language models (LLMs) are increasingly embedded in everyday decision-making, their safety responsibilities extend beyond reacting to explicit harmful intent toward anticipating unintended but consequential risks. In this work, we introduce a proactive risk awareness evaluation framework that measures whether LLMs can anticipate potential harms and provide warnings before damage occurs. We construct the Butterfly dataset to instantiate this framework in the environmental and ecological domain. It contains 1,094 queries that simulate ordinary solution-seeking activities whose responses may induce latent ecological impact. Through experiments across five widely used LLMs, we analyze the effects of response length, languages, and modality. Experimental results reveal consistent, significant declines in proactive awareness under length-restricted responses, cross-lingual similarities, and persistent blind spots in (multimodal) species protection. These findings highlight a critical gap between current safety alignment and the requirements of real-world ecological responsibility, underscoring the need for proactive safeguards in LLM deployment.