1 citations · 1 across the 10 of their papers we have counts for
10 papers
KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
Albert Sawczyn, Piotr Bielak, Tomasz Kajdanowicz
Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-grap…
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…
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction
Albert Sawczyn, Katsiaryna Viarenich, Konrad Wojtasik +4
Advancements in AI and natural language processing have revolutionized machine-human language interactions, with question answering (QA) systems playing a pivotal role. The knowled…
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…
Representation learning in multiplex graphs: Where and how to fuse information?
Piotr Bielak, Tomasz Kajdanowicz
In recent years, unsupervised and self-supervised graph representation learning has gained popularity in the research community. However, most proposed methods are focused on homog…
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…