Evaluating Proactive Risk Awareness of Large Language Models
arXiv:2602.20976v1 Announce Type: new Abstract: 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) speci
arXiv:2602.20976v1 Announce Type: new Abstract: 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.
Executive Summary
This study introduces a proactive risk awareness evaluation framework for large language models (LLMs) to anticipate potential harms and provide warnings before damage occurs. The framework is instantiated in the environmental and ecological domain using the Butterfly dataset, which contains 1,094 queries simulating ordinary solution-seeking activities. The study analyzes the effects of response length, languages, and modality on proactive awareness across five widely used LLMs. The results reveal consistent, significant declines in proactive awareness under length-restricted responses, cross-lingual similarities, and persistent blind spots in species protection. The findings highlight a critical gap between current safety alignment and ecological responsibility, underscoring the need for proactive safeguards in LLM deployment.
Key Points
- ▸ Introduction of a proactive risk awareness evaluation framework for LLMs
- ▸ Instantiation of the framework in the environmental and ecological domain using the Butterfly dataset
- ▸ Analysis of the effects of response length, languages, and modality on proactive awareness
Merits
Strength in theoretical contribution
The study provides a novel framework for evaluating proactive risk awareness in LLMs, which is a critical contribution to the field of AI safety and responsibility.
Strength in empirical contribution
The study uses a large and diverse dataset to analyze the effects of various factors on proactive awareness, providing a comprehensive understanding of the issue.
Demerits
Limitation in generalizability
The study focuses on a single domain (environmental and ecological) and may not be generalizable to other domains or contexts.
Limitation in scalability
The study uses a relatively small number of LLMs and may not be scalable to larger and more complex models.
Expert Commentary
This study is a significant contribution to the field of AI safety and responsibility, highlighting the critical need for proactive risk awareness and safety measures in the development and deployment of LLMs. The framework introduced in the study provides a valuable tool for evaluating proactive risk awareness, and the results of the analysis provide a comprehensive understanding of the issue. However, the study's limitations in generalizability and scalability should be addressed in future research. Additionally, the study's findings have important implications for policymakers and regulators, who must establish guidelines and standards for the development and deployment of LLMs to ensure ecological responsibility and safety.
Recommendations
- ✓ Developers and deployers of LLMs should prioritize proactive risk awareness and safety measures to mitigate potential harms.
- ✓ Policymakers and regulators should establish guidelines and standards for the development and deployment of LLMs to ensure ecological responsibility and safety.
