5 papers · 1 filter
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities
Hans Hergen Lehmann, Jae Hee Lee, Steven Schockaert +1
Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains…
Extracting Conceptual Spaces from LLMs Using Prototype Embeddings
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities and concepts using cognitively meaningful dimensions, typically referring to perceptual features. Such representations are widely used in cogni…
Ragas: Automated Evaluation of Retrieval Augmented Generation
Shahul Es, Jithin James, Luis Espinosa-Anke +1
We introduce Ragas (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG systems are compose…
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
Frank Mtumbuka, Steven Schockaert
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM). However, a key challenge arises from the fa…
Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
Conceptual spaces represent entities in terms of their primitive semantic features. Such representations are highly valuable but they are notoriously difficult to learn, especially…