News

Chaitanya Manapragada

Simon Razniewski (TU Dresden)

GPTKB: Comprehensively Materializing Factual LLM Knowledge

LLMs have majorly advanced NLP and AI, and next to their ability to perform a wide range of procedural tasks, a major success factor is their internalized factual knowledge. Since (Petroni et al., 2019), analyzing this knowledge has gained attention. However, most approaches investigate one question at a time via modest-sized pre-defined samples, introducing an “availability bias” (Tversky and Kahneman, 1973) that prevents the discovery of knowledge (or beliefs) of LLMs beyond the experimenter’s predisposition. To address this challenge, we propose a novel methodology to comprehensively materialize an LLM’s factual knowledge through recursive querying and result consolidation. As a prototype, we employ GPT-4o-mini to construct GPTKB, a large-scale knowledge base (KB) comprising 101 million triples for over 2.9 million entities. This work marks a milestone in two areas: For LLM research, for the first time, it provides constructive insights into the scope and structure of LLMs’ knowledge (or beliefs), and its strengths and weaknesses. For KB construction, it pioneers new pathways for the long-standing challenge of general-domain KB construction. GPTKB is accessible at https://gptkb.org.

Pierre Monnin (INRIA)

Gaël Varoquaux (INRIA)

Tabular foundation models: priors for numbers and strings

Deep-learning typically does not outperform tree-based models on tabular data. Often this may be explained by the small size of such datasets. For images, sound, text, the solution has be pretrained models, leading to foundation models, adapted and reused for many tasks. I will discuss the challenges to bring these ideas to tabular learning, and the progress that we have made, building priors for tables, ie columns of different natures, with numbers and strings.

Ken Satoh (National Institute of Informatics, Japan)

Translating German traffic cases into logical rules

This is a joint work with May Myo Zin at my center and Georg Borgess at University of Saarland. In this talk, I will report the work on extracting normative sentences from German traffic cases and translating them into logical rules. The development of autonomous vehicles (AVs) requires a comprehensive understanding of both explicit and implicit traffic rules to ensure legal compliance and safety. While explicit traffic laws are well-defined in statutes and regulations, implicit rules derived from judicial interpretations and case law are more nuanced and challenging to extract. This research firstly investigates the potential of Large Language Models (LLMs), particularly GPT-4o, in automating the extraction of implicit traffic normative sentences from judicial decisions. Then we investigate how to translate these normative sentences into a logical form. We explore to use large language models (LLMs) to automate the translation of traffic rules into PROLOG, a declarative programming language ideal for encoding logical rules and relationships. The proposed methodology consists of three key phases: extracting traffic rules from diverse textual sources, structuring them into Logical English (LE) for clarity and consistency, and translating them into PROLOG representations using advanced natural language processing (NLP) techniques, including in-context learning and fine-tuning. The experimental results demonstrate the effectiveness of LLMs in automating this process, achieving high accuracy in translation.

Fabian Suchanek

YAGO

In this talk I will present the newest version of YAGO, the knowledge base that we are building with several members of the DIG team. I will show why we build it, how we build it, and how it can be used. This will also be an occasion for me to get your feedback on our work.

Simon Delarue

Learning on graphs: from algorithms to socio-technical analyses on AI

This thesis addresses the dual challenge of advancing Artificial Intelligence (AI) methods while critically assessing their societal impact. With AI technologies now embedded in high-stake decision sectors like healthcare and justice, their growing influence demands thorough examination, reflected in emerging international regulations such as the AI Act in Europe. To address these challenges, this work leverages attributed-graph based methods and advocates for a shift from performance-focused AI models to approaches that also prioritise scalability, simplicity, and explainability.

The first part of this thesis develops a toolkit of attributed graph-based methods and algorithms aimed at enhancing AI learning techniques. It includes a software contribution that leverages the sparsity of complex networks to reduce computational costs. Additionally, it introduces non-neural graph models for node classification and link predictions tasks, showing how these methods can outperform advanced neural networks while being more computationally efficient. Lastly, it presents a novel pattern mining algorithm that generates concise, human-readable summaries of large networks. Together, these contributions highlight the potential of these approaches to provide efficient and interpretable solutions to AI’s technical challenges.

The second part adopts an interdisciplinary approach to study AI as a socio-technical system. By framing AI as an ecosystem influenced by various stakeholders and societal concerns, it uses graph-based models to analyse interactions and tensions related to explainability, ethics, and environmental impact. A user study explores the influence of graph-based explanations on user perceptions of AI recommendations, while the building and analysis of a corpus of AI ethics charters and manifestos quantifies the roles of key actors in AI governance. A final study reveals that environmental concerns in AI are primarily framed technically, highlighting the need for a broader approach to the ecological implications of digitalisation.

Lanfang Kong

Explainable algorithms for anomaly detection and time series forecasting

Artificial intelligence has shown dominant performance across diverse domains, including critical ones such as medicine, finance, justice and so on. As a result, the explainability of black-box models is becoming more and more important. We focus on two specific applications: anomaly detection and time series forecasting, and present XTREK and ADAPATCH, respectively.

XTREK is an unsupervised tree-based approach for explainable anomaly detection, which maximizes Kendall’s tau between the anomaly scores of the source anomaly detector and those of XTREK. The tree produced by our algorithm is relatively small in size, thereby boasting the renowned off-the-shelf transparency and explainability of tree-based approaches. Moreover, its explanations are sample-based. In particular, the anomaly scores are computed to be the inverse of the size of the corresponding leaf, thereby providing meaningful explanations when comparing examples with different anomaly scores. XTREK can also be used as an in-model approach, which is capable of providing concise explanations for its own decisions. Moreover, we propose efficient computation of Kendall’s tau coefficients when determining the best split at each node of the regression tree. We show how this can be computed incrementally, thereby making the running time of our algorithm almost linear (up to a logarithmic factor) in the size of the input.

ADAPATCH is an adaptive patch-based saliency map method for explainable time series forecasting, which provides local, post-hoc visualization explanations. The approach highlights those patches which would result in worse predictions when hidden to the black-box algorithm. With a differential encoding module in the mask of input, the optimization can be done by gradient-based perturbation. ADAPATCH does not need the patch parameters upfront, such as the length or the stride, as all patch-based approaches need. In fact, it learns those parameters from the data, thereby effectively adapting to different settings and application scenarios. By enforcing an upper bound on the maximum number of patches, we make sure that the patch-level explanations provided by our algorithm can be easily interpreted by humans, as opposed to explanations consisting of a large number of single time points. Moreover, ADAPATCH requires a much smaller number of parameters, typically linear in the number of patches as opposed to linear in the number of time steps. This makes our approach more efficient and easy to train.

Both methods are model-agnostic, which means the architecture of the black-box model can be hidden from the users. They provide accurate and simple explanations, as validated by extensive experiments.

Gabriel Damay

Dynamic Decision Trees and Community-based Graph Embeddings: towards Interpretable Machine Learning

Machine learning is the field of computer science that interests in building models and solutions from data without knowing exactly the set of instructions internal to these models and solutions. This field has achieved great results but is now under scrutiny for the inability to understand or audit its models among other concerns. Interpretable Machine Learning addresses these concerns by building models that are inherently interpretable. This thesis contributes to Interpretable Machine Learning in two ways.

First, we study decision trees. This is a very popular group of machine learning methods for classification problems and it is interpretable by design. However, real world data is often dynamic, but few algorithms can maintain a decision tree when data can be both inserted and deleted from the training set. We propose a new algorithm called FuDyADT to solve this problem.

Second, when data are represented as graphs, a very common machine learning technique called “embedding” consists in projecting them onto a vectorial space. This kind of method however is usually not interpretable. We propose a new embedding algorithm called PaRFaITe based on the factorization of the Personalized PageRank matrix. This algorithm is designed to provide interpretable results.

We study both algorithms theoretically and experimentally. We show that FuDyADT is at least comparable to state-of-the-art algorithms in the usual setting, while also being able to handle unusual settings such as deletions of data. PaRFaITe on the other hand produces embedding dimensions that align with the communities of the graph, making the embedding interpretable.