Machine learning is widely used for malware detection, but its decisions must be explained. An analyst needs to know whether a model has learned genuine malicious behavior or only dataset-specific patterns \cite{gaur2021semantics}. SHapley Additive exPlanations (SHAP) is the standard tool for this, backed by formal pro...
A dual-metric framework consisting of Abstention Rate (AR) and Hallucination Rate (HR) is proposed to characterize the trade-off between reliability and responsiveness to demonstrate that citation attribution systems should be evaluated not only for correctness but also for their ability to abstain appropriately under...
Deepa Tilwani, Yash Saxena, Seyedali Mohammadi et al.· 11 citations
Assessing suicide risk from social media text is a small-data, high-stakes setting requiring not only severity prediction but also supporting evidence and clinically relevant risk and protective factors. Yet common NLP techniques, including model scaling, synthetic data, loss reweighting, ensembling, and threshold tuni...
Shlok Shelat, Shrey Salvi, Souvik Roy et al.· 0 citations
Experiments on NSFW, artistic style, and object erasure, with a large-scale robustness-utility analysis over many CET baselines, show that PARSE erases multiple concepts robustly without sacrificing post-edit utility.
Results show CBT knowledge alone does not ensure effective application, giving the affective-computing community instrumentation to measure where LLMs fall short.
Vaishnavi Sinha, Pooja Guttal, Pranay Deep Reddy Katike et al.· 0 citations
SelfGraphRAG, a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever, suggests that knowledge graph structure can provide useful supervision for training graph retrievers when labeled data are unavailable.
Ben Lagnese, Manas Gaur· 0 citations
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