Raising the Bar for Chinese Adolescent LLM Safety: A Culturally-Grounded, Fine-Grained Benchmark
cs.CR
Submitted: 2026-09-28
Updated: 2026-09-28
Comments: 23 pages, 6 figures. Code and benchmark: https://github.com/WEILaboratory/QH-Bench
Code: https://github.com/WEILaboratory/QH-Bench
License: http://creativecommons.org/licenses/by/4.0/
The gist: Safety risks in conversations with adolescents are not always explicit.
Terminology
Abstract
Safety risks in conversations with adolescents are not always explicit. A request may appear harmless unless a model considers the user's age, circumstances, and earlier turns. Existing Chinese safety benchmarks mainly target general users and give limited attention to adolescent safety. Single-turn tests also miss risks that emerge over several turns. QH-Bench is a Chinese-language benchmark for adolescent content safety, with scenarios grounded in Chinese social and cultural settings. The single-turn track contains 715 test items organized into 10 risk domains, 50 subdomains, and 143 fine-grained risk scenarios. The multi-turn track contains 100 four-turn trajectories in a balanced 10-by-10 design that combines the same ten domains with ten cross-turn mechanisms. Both tracks use the same five-level safety-helpfulness scale and automatic judge, with track-specific criteria. Evaluation of 13 open-weight models identifies offline-contact scenarios as a shared weakness. Every model receives negative scores on more than half of the items involving offline meetings with online contacts, unfamiliar groups, and adults. This includes InternLM2.5-20B, the single-turn leader; negative scores indicate responses that partially or clearly facilitate risk. GLM-4-32B, the multi-turn leader, receives negative scores on 60% of complete trajectories in which users build relationships before invoking loyalty or confidentiality. These findings identify two priorities for the evaluated models: handling adolescent offline-contact risks and maintaining safety boundaries under relational pressure. Leading aggregate scores do not establish that these specific weaknesses have been resolved.
Sources
- The Age of Curiosity Meets the Age of AI: Benchmarking Child Safety in Large Language Models
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- Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions
- MinorBench: A hand-built benchmark for content-based risks for children
- CAREBench: A Child-Safety Risk Benchmark for Language Models
- ChildEval: When large language models meet children's personalities
- HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
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- YouthSafe: A Youth-Centric Safety Benchmark and Safeguard Model for Large Language Models
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