In this paper, we propose Knowledge Base augmented Language Model (KBLAM), a new method for augmenting Large Language Models (LLMs) with external knowledge. KBLAM works with a knowledge base (KB) constructed from a corpus of documents, transforming each piece of knowledge in the KB into continuous key-value vector pairs via pre-trained sentence encoders with linear adapters and integrating them into pre-trained LLMs via a specialized rectangular attention mechanism. Unlike Retrieval-Augmented Generation, KBLAM eliminates external retrieval modules, and unlike in-context learning, its computational overhead scales linearly with KB size rather than quadratically. Our approach enables integrating a large KB of more than 10K triples into an 8B pre-trained LLM of only 8K context window on one single A100 80GB GPU and allows for dynamic updates without model fine-tuning or retraining. Experiments demonstrate KBLAM’s effectiveness in various tasks, including question-answering and open-ended reasoning, while providing interpretable insights into its use of the augmented knowledge. Code and datasets are available at https://github.com/microsoft/KBLaM/

Recent advances in machine learning have significantly impacted the field of information extraction, with Language Models (LMs) playing a pivotal role in extracting structured information from unstructured text.Prior works typically represent information extraction as triplet-centric and use classical metrics such as precision and recall for evaluation.We reformulate the task to be entity-centric, enabling the use of diverse metrics that can provide more insights from various perspectives.We contribute to the field by introducing Structured Entity Extraction and proposing the Approximate Entity Set OverlaP (AESOP) metric, designed to appropriately assess model performance.Later, we introduce a new Multistage Structured Entity Extraction (MuSEE) model that harnesses the power of LMs for enhanced effectiveness and efficiency by decomposing the extraction task into multiple stages.Quantitative and human side-by-side evaluations confirm that our model outperforms baselines, offering promising directions for future advancements in structured entity extraction.Our source code is available at https://github.com/microsoft/Structured- Entity-Extraction.

Redundancy concepts are major design drivers in fault-tolerant space systems. It can be a difficult task to decide when to activate which redundancy, and which component should be replaced. In this paper, we refine a methodology where recovery strategies are synthesized from a model of non-deterministic dynamic fault trees. The synthesis is performed by transforming non-deterministic dynamic fault trees into Markov automata that represent all possible choices between recovery actions. From the corresponding scheduler, optimized for maximum expected long-term reachability of failure states, a recovery strategy, optimal with respect to mean time to failure, can then be derived and represented by a model we call recovery automaton. We discuss techniques for reducing the state space of this recovery automaton, and analyze their soundness and completeness. We show that they do not generally guarantee recovery automata with the minimal number of states and derive a class where this guarantee holds. Implementation details for our approach are given and its effectiveness is verified on the basis of three case studies.