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Sign language translation (SLT) converts continuous sign videos into spoken language text. Gloss-free approaches leverage pre-trained visual encoders and language models but rely on implicit cross-modal alignment from translation supervision alone.
Abstract As large language models (LLMs) integrate into critical decision-making, their alignment with human values in high-stakes scenarios remains unclear. This study systematically investigates LLM behavioral consistency, focusing on cooperative intent, resource distribution, and moral reasoning, under simulated emergencies. We employed established psychological scales in two crisis scenarios: natural disaster resource allocation and crowd panic response.
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