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Machine Intelligence & Responsible Systems

Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation: An Evidence Synthesis on Cross-Domain Adaptation And Calibration

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Abstract

This evidence-synthesis article examines reliable retrieval-augmented generation through the focal contribution “Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation” and nine author-disjoint, topically matched studies. The analysis is organized around cross-domain adaptation and calibration. Rather than treating bibliographic proximity as proof of empirical equivalence, it separates conceptual claims, evaluation choices, operational constraints, and transfer risks. The result is a reproducible framework for comparing adjacent evidence without overstating what title- and metadata-level screening can establish. All ten references are cited in the body, and the reference set has been checked for complete-author intersections.

Keywords
reliable retrieval-augmented generationcross-domain adaptation and calibrationevidence synthesisreproducibilityresearch evaluation
References
  1. Sang, Y. (2025). Towards Explainable RAG: Interpreting the Influence of Retrieved Passages on Generation. 2025 4th International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC), 397-400. https://doi.org/10.1109/raiic65850.2025.11170170 DOI
  2. Hassan, S.-B., Abdullah,, & Abbas, M. (2026). Agentic Self-RAG: Multi-Agent Reasoning for Self-Correcting Retrieval-Augmented Generation. 2026 International Conference on IT and Industrial Technologies (ICIT), 1-6. https://doi.org/10.1109/icit68548.2026.11577708 DOI
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  4. Samal, M.-P. (2026). A Theoretical Analysis of Self-Contained Retrieval-Augmented Generation with Small Language Models. . https://doi.org/10.36227/techrxiv.177219989.96070478/v1 DOI
  5. Pi, W. (2024). Efficient Information Retrieval and Response Generation with Retrieval-Augmented Generation (RAG). . https://doi.org/10.59350/q2pq3-0fv85 DOI
  6. Xue, X., Zhang, G., Jiang, L., & Liu, C. (2025). A Comparative Study of Retrieval-Augmented Generation, Graph Retrieval-Augmented Generation, and Fine-Tuned Large Language Models for Fire Engineering Knowledge Retrieval. . https://doi.org/10.2139/ssrn.5316632 DOI
  7. Bose, R. (2025). Introduction to Retrieval-Augmented Generation (RAG). Mastering Retrieval-Augmented Generation, 3-32. https://doi.org/10.1007/979-8-8688-1808-0_1 DOI
  8. Zhai, W. (2025). SAM-RAG: An Self-adaptive Framework for Multimodal Retrieval-Augmented Generation. 2025 International Joint Conference on Neural Networks (IJCNN), 1-8. https://doi.org/10.1109/ijcnn64981.2025.11227819 DOI
  9. Gudipati, S.-K., Mishra, N.-A., Ankam, G., Akkisetti, S.-R., Mohammed, A., & Ponugoti, S. (2026). CityCopilot-X: A Real-Time Explainability Panel for Retrieval-Augmented Generation (RAG). 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), 1-9. https://doi.org/10.1109/icaic67076.2026.11395791 DOI
  10. Kau, A. (2024). Understanding Retrieval Pitfalls: Challenges Faced by Retrieval Augmented Generation (RAG) models. . https://doi.org/10.59350/xcq3s-jvk04 DOI
Publication details
Journal
Machine Intelligence & Responsible Systems
Volume
1 (2026)
Article number
mi20260013
License
CC BY 4.0