Miguel Couceiro – Universidade de Lisboa
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Bernhard Ganter – Technische Universität Dresden
Essential Concepts
Essential Concepts is an experimental format that is being tried out for the fi rst time. Instead of a one-hour presentation, I will be offering a series of short talks spread out over several days. I will try to highlight some important aspects of Formal Concept Analysis that, in my opinion, deserve more attention, and I hope to rekindle interest in them. If the Essential Concepts format proves popular, other topics may be covered in the future.
Andreas Hotho – University of Würzburg
From German Language Models to Conceptual Knowledge Structures
Large language models have become powerful tools for representing and generating language in chat-based systems, but the relation between their learned representations and explicit conceptual structures remains only partially understood. Their simple training objective is astonishingly effective and leads to a large amount of knowledge being stored in the weights of such models. A lot of research is currently being conducted to understand the implicit learned representations and conceptualizations of these models, as well as to connect internal representations with explicit concepts. In this talk, I will start from our recent work on LL=C3=A4Mmlein and ModernGBERT, two transparent German model families trained from scratch, and discuss how language-specific models can be built, evaluated, and used. These models provide a controlled basis for studying how language-specific representations emerge from data, training pipelines, tokenization choices, and model architecture, and allow us to explore the relation between emergent and explicit conceptual structures.
Recent work on attribution graphs suggests that large language models may form internal feature structures and computational pathways that resemble concept-like organization. Such analyses reveal intermediate representations, multi-step mechanisms, and abstract features inside the model. Concepts in language models are present, can be used and extracted, but they are not directly available as explicit, stable, inspectable objects or formal concepts. In the talk, I will show the relation between these learned representations and explicit concepts.
I will then connect this view to our work on enriching language models with knowledge graphs for computational literary studies. In this setting, concepts are not only factual entities, but historically changing, culturally situated, and domain-specific structures of meaning. Dictionaries, encyclopedias, lexicons, and literary texts can be transformed into graph-based representations, i.e., knowledge graphs, that capture semantic relations, diachronic concept alignment, character networks, plot structures, and other forms of conceptual knowledge. These resources can enrich language models and support semantically richer representations, especially in domains with limited computational resources.
The talk argues for a concept-centered perspective on language models: learned representations reveal that models organize language through internal concept-like structures, while knowledge graphs and related conceptual representations make selected parts of this structure explicit, inspectable, and reusable. Connecting both perspectives opens a path toward concept-aware language technologies that combine the flexibility of modern language models with explicit structures of meaning.
Fatiha Sais – Paris Saclay University
Title TBA
Abstract TBA