Large Language Models (LLMs) can generate natural language explanations that rationalize their own decisions, a phenomenon commonly referred to as self-explanations.Such explanations have emerged as a promising direction for explainable artificial intelligence (XAI), particularly for interpreting LLM behavior.However, while self-explanations often appear plausible, whether they faithfully reflect a model's underlying reasoning process remains an open question. In this opinion paper, we argue that self-explanations can be highly plausible, questionably faithful, and yet highly actionable. From a traditional XAI perspective, we identify the limitations of standard evaluation protocols for LLM-generated self-explanations and propose practical guidelines for assessing their plausibility and faithfulness. Moreover, we argue that evaluation should extend beyond these criteria to actionability, highlighting applications of LLM rationalization capabilities that support informed decision-making and appropriate action across diverse stakeholders.
E. Herrewijnen, Benedetta Muscato, Gizem Gezici et al.· 0 citations
Faithfulness hallucinations, where large language models generate outputs unsupported by retrieved evidence, remain a central challenge for trustworthy AI. We present a systematic empirical evaluation of faithfulness in retrieval-augmented generation (RAG) systems using two benchmark datasets, HotpotQA and HaluBench, covering both multi-hop reasoning and single-hop hallucination detection. We analyze three small-to-mid-sized (2B-8B) 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. By disentangling retrieval and generation errors, we characterize how different pipeline components contribute to hallucinations in RAG systems. Our analysis provides actionable insights and practical evaluation protocols, highlighting the critical role of robust retrieval and careful system design. These findings offer a benchmarking-oriented perspective for developing more reliable and faithful RAG systems within evaluated model scales.
C. Mala, Gizem Gezici, Fosca Giannotti· Machine-mediated learning· 0 citations