Proposes SCSuff metric for evaluating LLM explanation sufficiency via model-generated alternative inputs, showing explanations are typically insufficient and predictable from hidden states.
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7 Pith papers cite this work. Polarity classification is still indexing.
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Pruning attention layers in five LLMs across eight datasets maintains accuracy but degrades faithfulness and calibration.
CERA fine-tunes a dense retriever with triplet contrastive learning plus attention alignment to human rationales, claiming better retrieval effectiveness and faithfulness on clinical trial reports than Contriever and standard hard-negative baselines.
PrimeFacts extracts decontextualized premises from fact-check articles, raising evidence retrieval MRR by up to 30% and verdict prediction Macro-F1 by 10-20 points over baselines.
EPPC-OASIS combines ontology-aware fine-tuning via Wasserstein alignment with structured inference refinement to extract EPPC codes from secure messages, reporting 77.13% Code+Sub-code F1 and 63.83% Triplet F1 with small gains over supervised fine-tuning baselines.
NEURON integrates SNOMED CT, ML, and RAG LLM to raise AUC from 0.74-0.77 to 0.84-0.88 and human-aligned explainability scores from 0.50 to 0.85 on MIMIC-IV acute heart failure data.
An off-the-shelf LLM prompted on tokenized Modbus traffic from public ICS datasets matches supervised baselines in normal-versus-critical classification accuracy while generating token-grounded audit records without any model updates.
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Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems
An off-the-shelf LLM prompted on tokenized Modbus traffic from public ICS datasets matches supervised baselines in normal-versus-critical classification accuracy while generating token-grounded audit records without any model updates.