1 citations · 1 across the 10 of their papers we have counts for
10 papers
Schematize: An Agentic System for Generating and Refining Information-Extraction Schemas for Legal Research
Albert Sawczyn, Jakub Binkowski, Kamil Tagowski +3
Empirical legal research often relies on turning research questions into structured data extracted from large collections of rulings and judgments. Designing the extraction schema…
Attention Sinks as Internal Signals for Hallucination Detection in Large Language Models
Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz
Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination d…
A Geometry-Based View of Mahalanobis OOD Detection
Denis Janiak, Jakub Binkowski, Tomasz Kajdanowicz
Out-of-distribution (OOD) detection is critical for reliable deployment of vision models. Mahalanobis-based detectors remain strong baselines, yet their performance varies widely a…
The Illusion of Progress: Re-evaluating Hallucination Detection in LLMs
Denis Janiak, Jakub Binkowski, Albert Sawczyn +3
Large language models (LLMs) have revolutionized natural language processing, yet their tendency to hallucinate poses serious challenges for reliable deployment. Despite numerous h…
FactSelfCheck: Fact-Level Black-Box Hallucination Detection for LLMs
Albert Sawczyn, Jakub Binkowski, Denis Janiak +2
Large Language Models (LLMs) frequently generate hallucinated content, posing significant challenges for applications where factuality is crucial. While existing hallucination dete…
Hallucination Detection in LLMs Using Spectral Features of Attention Maps
Jakub Binkowski, Denis Janiak, Albert Sawczyn +2
Large Language Models (LLMs) have demonstrated remarkable performance across various tasks but remain prone to hallucinations. Detecting hallucinations is essential for safety-crit…
Empowering Small-Scale Knowledge Graphs: A Strategy of Leveraging General-Purpose Knowledge Graphs for Enriched Embeddings
Albert Sawczyn, Jakub Binkowski, Piotr Bielak +1
Knowledge-intensive tasks pose a significant challenge for Machine Learning (ML) techniques. Commonly adopted methods, such as Large Language Models (LLMs), often exhibit limitatio…
Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning
Denis Janiak, Jakub Binkowski, Piotr Bielak +1
In recent years, self-supervised learning has played a pivotal role in advancing machine learning by allowing models to acquire meaningful representations from unlabeled data. An i…
Graph-level representations using ensemble-based readout functions
Jakub Binkowski, Albert Sawczyn, Denis Janiak +2
Graph machine learning models have been successfully deployed in a variety of application areas. One of the most prominent types of models - Graph Neural Networks (GNNs) - provides…
RAFEN -- Regularized Alignment Framework for Embeddings of Nodes
Kamil Tagowski, Piotr Bielak, Jakub Binkowski +1
Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the…