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: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
arXiv:2606. 07822v1 Announce Type: cross Abstract: As language models improve and become increasingly deployed to solve a variety of tasks, trustworthiness becomes essential.
By Nishant Subramani, Palash Goyal, Yiwen Song, Mani Malek, Yuan Xue, Tomas Pfister, Hamid Palangi
arXiv:2607. 29614v1 Announce Type: cross Abstract: The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI).
By Christian Oliva, Luis F. Lago-Fern\'andez
arXiv:2608. 17183v1 Announce Type: new Abstract: Small Language Models (SLMs) are increasingly deployed in resource-constrained, privacy-sensitive settings, where safety and bias failures can cause security and societal risks.
By Nyamtulla Shaik, Fengjun Li, Bo Luo