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The paper proposes RMS-RSP, a perturbation-sensitive method for selecting medical questions to receive rationale supervision, improving robust accuracy and semantic consistency in QA systems.
This paper introduces VERDICT, a neuro-symbolic system that uses LLMs and SMT solvers to ensure accountable decision-making in clinical trial matching, providing consistent policies and faithful rationales.
This paper analyzes safety-tuning in large language models, showing that decomposing responses into refusal statements and rationales reveals that training on rationales alone reduces false refusals while preserving safety, improving the balance between helpfulness and safety.
This paper proposes a pipeline for fine-tuning LLMs specifically for explainable misinformation detection and introduces LonsRex, a data synthesis method to generate necessary and sufficient rationales, addressing limitations of naive filtering based solely on label correctness.