arXiv:2501.14940v4 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refusal of individual problematic queries, which overlooks the importance of the context where the query occurs and may cause undesired refusal of queries under safe contexts that diminish user experience. Addressing this gap, we introduce CASE-Bench, a Context-Aware SafEty Benchmark that integrates context into safety assessments of LLMs. CASE-Bench assigns disti
The rapid deployment of LLMs necessitates more sophisticated safety mechanisms to prevent unintended negative consequences and ensure user trust, leading to the development of context-aware benchmarks.
This benchmark addresses a critical limitation in current LLM safety, moving beyond simplistic query-based refusal to a more nuanced understanding of context, which is essential for responsible AI development and broader adoption.
LLM safety evaluation will become more sophisticated, potentially leading to models that are less prone to unwarranted refusals and better integrated into complex applications, improving user experience and utility.
- · AI developers focused on ethical deployment
- · Enterprises integrating LLMs into sensitive applications
- · Users of LLMs requiring reliable and nuanced interactions
- · LLM developers who prioritize raw capability over safety
- · Benchmarks that rely solely on decontextualized safety checks
- · Bad actors seeking to exploit LLMs through context manipulation
Improved safety and reliability of LLMs in diverse real-world applications by incorporating contextual understanding.
Increased user trust and broader adoption of LLM-powered services, particularly in regulated industries.
The development of more sophisticated AI 'common sense' or ethical reasoning modules that incorporate an understanding of situations beyond mere linguistic prompts.
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Read at arXiv cs.AI