Explainability for transformer-based language models is becoming crucial as these systems are deployed in high-stakes applications. As a result, the ecosystem of explainability tools is rapidly evolving, becoming richer, but also more fragmented and harder to navigate. To address this challenge, we present Virgil, an i...
Martino Ciaperoni, Sezer Kutluk, Benedetta Muscato et al.· 0 citations
It is argued that self-explanations can be highly plausible, questionably faithful, and yet highly actionable, and evaluation should extend beyond these criteria to actionability, highlighting applications of LLM rationalization capabilities that support informed decision-making and appropriate action across diverse st...
E. Herrewijnen, Benedetta Muscato, Gizem Gezici et al.· arXiv.org· 0 citations
This work analyzes three small-to-mid-sized open-weight LLMs in combination with multiple retrieval strategies, including sparse, dense, and hybrid approaches, as well as score-based and rank-based fusion techniques, enabling a comprehensive assessment of retrieval-generation interactions.
C. Mala, Gizem Gezici, Fosca Giannotti· Machine-mediated learning· 0 citations
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