As Large Language Models (LLMs) are increasingly applied to document-based tasks - such as document summarization, question answering, and information extraction - where user requirements focus on retrieving information from provided documents rather than relying on the model's parametric knowledge, ensuring the trustworthiness and interpretability of these systems has become a critical concern. A central approach to addressing this challenge is attribution, which involves tracing the generated outputs back to their source documents. However, since LLMs can produce inaccurate or imprecise responses, it is crucial to assess the reliability of these citations. To tackle this, our work proposes two techniques. (1) A zero-shot approach that frames attribution as a straightforward textual entailment task. Our method using flan-ul2 demonstrates an improvement of 0.27% and 2.4% over the best baseline of ID and OOD sets of AttributionBench, respectively. (2) We also explore the role of the attention mechanism in enhancing the attribution process. Using a smaller LLM, flan-t5-small, the F1 scores outperform the baseline across almost all layers except layer 4 and layers 8 through 11.

In this paper, we develop a framework to obtain graph abstractions for decision-making by an agent where the abstractions emerge as a function of the agent's limited computational resources. We discuss the connection of the proposed approach with information-theoretic signal compression, and formulate a novel optimization problem to obtain tree-based abstractions as a function of the agent's computational resources. The structural properties of the new problem are discussed in detail, and two algorithmic approaches are proposed to obtain solutions to this optimization problem. We discuss the quality of, and prove relationships between, solutions obtained by the two proposed algorithms. The framework is demonstrated to generate a hierarchy of abstractions for a non-trivial environment.

As more and more devices rely on data sharing, the Internet of Things (IoT) is rapidly expanding. Developing strong privacy and security protocols for these systems is critical, as devices, applications, and communication networks become increasingly integrated. However, as these systems grow, cyber attacks on the IoT are becoming increasingly complex. These attacks often involve machine learning, earning themselves the name "smart attacks". The security solutions necessary to combat these attacks are computationally heavy and frequently involve a large communication load. Additionally, due to their relatively small computational abilities, many IoT devices are more vulnerable to attacks than computer systems.

Stuart Russell is a leading researcher in the field of artificial intelligence. He is a Professor of Computer Science at the University of California at Berkeley, Associate Editor of the Journal of the ACM, and author of "Artificial Intelligence: A Modern Approach" (Prentice Hall, 1995, 2003), the leading textbook in the field. His research interests include machine learning, limited rationality, real-time decision-making, intelligent agent architectures, autonomous vehicles, search, game-playing, reasoning under uncertainty, and commonsense knowledge representation. UBIQUITY: The original grand vision of artificial intelligence (AI) in the 1950s and '60s seemed to dissipate into many small, disparate projects. Should this fragmentation be written off as an inevitable Humpty-Dumpty problem or is it possible to bring the fragments back together into a single field? RUSSELL: I think we can put it back together in the sense of being able to join the pieces.