11 accepted papers
Large Language Models (LLMs) have achieved remarkable success in software engineering tasks when trained with executable runtime environments, particularly in resolving GitHub issues. However, such runtime environments are often unavailable in other domains, especially cybersecurity, where challenge…
Large language models (LLMs) have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting progre…
Retrieval-augmented generation (RAG) systems face a fundamental challenge in aligning independently developed retrievers and large language models (LLMs). Existing approaches typically involve modifying either component or introducing simple intermediate modules, resulting in practical limitations a…
DatalogMTL is a powerful rule-based language for temporal reasoning. Due to its high expressive power and flexible modeling capabilities, it is suitable for a wide range of applications, including tasks from industrial and financial sectors. However, due its high computational complexity, practical…
Handling drafty partial code remains a notable challenge in real-time code suggestion applications. Previous work has demonstrated shortcomings of large language models of code (CodeLLMs) in completing partial code with potential bugs. In this study, we view partial code as implementation hints and…
Working memory is a critical aspect of both human intelligence and artificial intelligence, serving as a workspace for the temporary storage and manipulation of information. In this paper, we systematically assess the working memory capacity of ChatGPT, a large language model developed by OpenAI, by…
As a powerful framework for graph representation learning, Graph Neural Networks (GNNs) have garnered significant attention in recent years. However, to the best of our knowledge, there has been no formal analysis of the logical expressiveness of GNNs as Boolean node classifiers over multi-relationa…
The problem of answering complex First-order Logic queries over incomplete knowledge graphs is receiving growing attention in the literature. A promising recent approach to this problem has been to exploit neural link predictors, which can be effective in identifying individual missing triples in t…
DatalogMTL is a powerful extension of Datalog with operators from metric temporal logic (MTL), which has received significant attention in recent years. In this paper, we investigate materialisation-based reasoning (a.k.a. forward chaining) in the context of DatalogMTL programs and datasets with bou…
DatalogMTL is an extension of Datalog with operators from metric temporal logic which has received significant attention in recent years. It is a highly expressive knowledge representation language that is well-suited for applications in temporal ontology-based query answering and stream processing.…
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design four modules (for clas- sification and sequence labelling) to jointly extract dialogue states. Experimental results based…