Across eight LLMs, most explicitly IHL-violating prompts are refused, and a single system-level safety prompt raises explanatory refusal rates in six of eight models, though the benchmark is not publicly released.
Milaninia, ‘Biases in machine learning models and big data analytics: The inter- national criminal and humanitarian law im- plications’, Int
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CY 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
From Rogue to Safe AI: The Role of Explicit Refusals in Aligning LLMs with International Humanitarian Law
Across eight LLMs, most explicitly IHL-violating prompts are refused, and a single system-level safety prompt raises explanatory refusal rates in six of eight models, though the benchmark is not publicly released.