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Experiments show that the proposed Preference Vector framework improves helpfulness without excessive conservatism, allows smooth control over preference trade-offs, and supports scalable multi-preference alignment.
This work introduces ConstructCIE, a manually annotated dataset for Causal Information Extraction from OSHA construction accident reports, and evaluates supervised sequence taggers and instruction-tuned LLMs in an end-to-end hierarchical extraction setting.
PEPPER (PErcePtion-Guided PERturbation), a backdoor defense that rewrites the caption into a semantically distant yet visually similar caption while adding unobtrusive elements, achieves enhanced robustness without training or access to model weights.
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