arXiv:2608.21803v1 Announce Type: cross
Abstract: As machine learning (ML) models are increasingly deployed in high-stakes environments, explainable AI (XAI) methods like SHAP and LIME have become es...
By Maraz Mia, Shovan Roy, Mir Mehedi A. Pritom, Maanak Gupta
arXiv:2605. 16651v2 Announce Type: replace-cross Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model decisions require human oversight.
By Narges Babadi, Hadis Karimipour
arXiv:2410. 14573v2 Announce Type: replace-cross Abstract: Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications.
By Nazanin Nezami, Hadis Anahideh
arXiv:2609.37040v1 Announce Type: cross
Abstract: Natural language autoencoders translate a language model's internal activations into readable explanations. Explaining every token position is costly...
By Federico Torrielli, Gianluca Barmina, Andrea Blasi N\'u\~nez, Amon Rapp, Luigi Di Caro, Peter Schneider-Kamp, Lukas Galke Poech
The paper introduces Trustworthy RAG, an evaluation agent designed to detect misinformation and knowledge poisoning in Retrieval-Augmented Generation systems. It combines natural language inference verification, a five-signal poison detector, and a weighted Trust Index to assess the reliability of retrieved content. Experiments on multiple LLMs show high accuracy and precision, with the agent effectively blocking unsafe advice in a secure-coding assistant scenario.
By Balkrishna Giri, Md Toufique Hasan, Jussi Rasku, Muhammad Waseem, Pekka Abrahamsson
arXiv:2510. 21891v2 Announce Type: replace-cross Abstract: To deploy large language models (LLMs) in high-stakes application domains that require substantively accurate responses to open-ended prompts, we need reliable, computationally inexpensive methods that assess the trustworthiness of long-form responses generated by LLMs.
By Dhrupad Bhardwaj, Julia Kempe, Tim G. J. Rudner