Counterfactual explanations (CFEs) are essential for interpreting machine learning model predictions by illustrating how minimal input changes can alter outcomes in counterfactual scenarios. Effective CFEs should satisfy two key properties: feasibility and diversity. Feasibility ensures that the generated CFEs are realistic and applicable across various contexts, while diversity provides multiple perspectives on model predictions. However, existing methods face significant limitations, such as difficulty in generating multiple CFEs and reliance on a convex loss landscape, which hinders adaptability to complex scenarios and diverse applications. To overcome these challenges, we propose a novel approach for generating diverse CFEs applicable to both convex and non-convex machine learning models. Our method leverages a Farthest Point Sampling-enhanced Genetic Algorithm to improve diversity, while a sample-based Manhattan distance regularization enhances feasibility. Experimental results demonstrate that our approach outperforms state-of-the-art methods in generating CFEs that are both feasible and diverse.
The fast progress of large language models (LLMs) opens new opportunities in the management of cyber threat intelligence, but their reliability for operational tasks remains unclear. In this work, we benchmark LLMs on the task of threat level determination. First, we construct a curated dataset derived from publicly available MISP OSINT feeds. Next, we design a tailored prompt to systematically compare eight different LLMs under zero-shot conditions. Finally, we apply supervised fine-tuning on each model and perform a comparative analysis between baseline and fine-tuned versions. Our results show that zero-shot models achieve weak performance, with limited ability to correctly assign threat levels. Fine-tuned models, however, demonstrate substantial improvements, reaching F1 scores between 0.40 and 0.58 depending on the base architecture. Despite this progress, the performance is still low for practical deployment, highlighting the need for additional research on data quality, model adaptation, and domain-specific tuning.
Machine Learning as a Service (MLaaS) systems face a fundamental tension between regulatory demands for explainability and security vulnerabilities from model extraction attacks. While global regulations increasingly mandate transparent AI systems, emerging evidence suggests increasing model explainability can increase the vulnerability to model extraction attacks. This paper advocates for investigating the underexplored relationship between model extractability and explainability. Like how bulletproof glass provides visibility without vulnerability, we envision theoretical frameworks enabling MLaaS systems to achieve human-level explainability without enabling extraction attacks. Impeding this vision, we identify three critical technical gaps: the absence of general frameworks to predict extraction risk, standardized extractability metrics, and predictive security approaches. Institutional fragmentation across model owners, security teams, explainability researchers, and regulators further blocks coordinated solutions. The urgency of this agenda stems from rapid MLaaS adoption, expanding regulatory mandates, and increasing AI-related security incidents. Without an explainability-extractability framework, organizations must choose not to comply with regulatory transparency requirements or face exploitation through model theft and training data extraction. Success in this area can extend to complex systems beyond AI, from financial markets to government operations, wherever transparency is desirable but potentially dangerous.
Large language models (LLMs)-based agents are transforming many fields due to their great potential in automating complex tasks. Despite great success, their application in pharmaceutical data analysis remains hindered by coarse-grained workflows and potential data safety risks. General-purpose agents often produce unreliable results and operate with high-risk tools, failing to meet the rigorous demands of data analysis in the pharmaceutical domain. To bridge this gap, we developed PharmaData-Agent, an agent designed specifically for pharmaceutical statisticians. PharmaData-Agent automates the data analysis pipeline through a four-step workflow: data parsing, data understanding, data analysis, and data visualization. Furthermore, by orchestrating sophisticated planning and reflection with specialized subagents, PharmaData-Agent transforms complex tasks into a transparent, multistep process. Empirical evaluations show that PharmaDataAgent achieves perfect tool-calling performance and 95.29% accuracy on DaBench, highlighting its potential to empower statisticians with reliable analytical capability.
The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all candidates due to computational constraints. Despite the recent advances in LLM selection, a fundamental research question largely remains nascent: how can we model the dynamic behaviors of LLMs during fine-tuning, thereby enhancing our understanding of their generalization performance across diverse downstream tasks? In this work, we propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs, thereby enabling accurate and efficient LLM selection for downstream applications. In particular, we first derive a PAC-Bayesian Generalization Bound that unveils fine-tuning dynamics of LLMs and then introduce LensLLM, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions across diverse tasks while maintaining computational efficiency. Extensive empirical results on 3 large-scale benchmarks demonstrate that our model achieves up to 91.1% accuracy and reduces up to 88.5% computational cost in LLM selection, outperforming 5 state-of-the-art methods. We open-source our proposed LensLLM model and corresponding results at LensLLM.io.
