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arXiv AI By Aisvarya Adeseye, Adeyemi Adeseye, Mohammad Tahir, Jouni Isoaho

CARE-SD: Explainable Reasoning Architecture for Local SLM Self-Disclosure Analysis in Textual Data

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The paper introduces CARE‑SD, a four‑module architecture designed to enhance the reasoning and explanation capabilities of small language models (SLMs) for analyzing self‑disclosure in cybersecurity interview transcripts. By integrating context‑adaptive processing, hierarchical disclosure reasoning, explanation‑consistency verification, and cross‑batch reconciliation, CARE‑SD improves the accuracy and evidence reliability of SLMs, raising Llama 3B’s Macro‑F1 from 0.80 to 0.88 and cutting unsupported claims by 60%. The study demonstrates that a 3B model can match the performance of a 20B reference model, highlighting the architecture’s effectiveness without needing larger models.

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