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Generative large language models (LLMs) inherit undesirable behaviors from pre-training, including demographic bias and toxic generation, that often emerge only after deployment and affect a small subset of inputs. A repair should eliminate the identified defect, preserve the model's overall functionality and, ideally,...
Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies. This distinction is critical for behavioral simulation, where actions are often shaped by prior context rather than marg...
A common rank-displacement audit at three scopes is introduced at three scopes: facts supported by the edited parameter, other correct answers to the target query, and correct answers across queries with the same relation.
DIASENTINEL demonstrates a practical framework for reliable, auditable, and privacy-preserving LLM-based clinical decision support for type 2 diabetes mellitus risk screening and guideline-grounded report generation from electronic health records (EHRs).
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