ABench: Adversarily Benchmarking Large Language Models across Taxonomized Values
To address these limitations, we propose ABench, a fine-grained, handcrafted benchmark specifically tailored to taxonomized values in Chinese.
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Key points
- Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment.
- Moreover, they often fail to effectively evaluate model robustness against complex, adversarial inductions within specific cultural contexts.
- Furthermore, we propose a weighted automated evaluation framework that utilizes question-specific scoring criteria for nuanced assessment.
- Our data will be publicly released to promote further research on taxonomized value alignment.
Sources (1)
- [1]ABench: Adversarily Benchmarking Large Language Models across Taxonomized ValuesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 04:23 AM
To address these limitations, we propose ABench, a fine-grained, handcrafted benchmark specifically tailored to taxonomized values in Chinese.
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment.
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