Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models

Key points

• When a large language model refuses an unethical request, what does refusal mean?1
• In conversation, refusal is bounded by what the model says; once the model is equipped with executable tools, what it does can diverge from what it says, a failure mode that English-only evaluations cannot fully reveal.1
• Judgments come from a reasoning-model judge (o4-mini), calibrated against one Portuguese-speaking author ($n=83$; harmful/non-harmful $κ=0.78$), compared with two other provider judges (Fleiss' $κ=0.78$, $n=607$), and complemented by retrospective sandbox replay.1
• Matched benign controls demonstrate legitimate task completion in 82.8% of 3,300 trials.1

Sources (1)

• [1]Lumen-bench: Cross-Lingual Safety Gaps and the Say-Do Problem in Tool-Calling Large Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 07:19 PM

  We introduce Lumen-bench, a behavioral benchmark of tool-calling ethics spanning 30 models, five languages (Portuguese, English, Spanish, Mandarin Chinese, Hindi), 108 Portuguese-authored scenarios, six social-pressure conditions, two system prompts (ethical and neutral), and 21 generic tools.

  When a large language model refuses an unethical request, what does refusal mean?

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