1 citations · 2 across the 6 of their papers we have counts for
6 papers
LLMs Contain Multitudes: How Deployment Context Reshapes Model-Level Preferences and Values
Filip Trhlik, Aoife O'Flynn, Angela Yu +2
Large language models (LLMs) are increasingly characterised in recent evaluation work as having stable, model-level preference and value systems. However, accompanying robustness c…
Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing
Filip Trhlik, Andrew Caines, Paula Buttery
Pre-trained language models (LMs) have, over the last few years, grown substantially in both societal adoption and training costs. This rapid growth in size has constrained progres…
Rethinking AI Cultural Alignment
Michal Bravansky, Filip Trhlik, Fazl Barez
As general-purpose artificial intelligence (AI) systems become increasingly integrated with diverse human communities, cultural alignment has emerged as a crucial element in their…
Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles
Filip Trhlik, Pontus Stenetorp
Large language models (LLMs) are increasingly being utilised across a range of tasks and domains, with a burgeoning interest in their application within the field of journalism. Th…
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
Liam Dugan, Alyssa Hwang, Filip Trhlik +5
Many commercial and open-source models claim to detect machine-generated text with extremely high accuracy (99% or more). However, very few of these detectors are evaluated on shar…
Fine-grained Czech News Article Dataset: An Interdisciplinary Approach to Trustworthiness Analysis
Matyáš Boháček, Michal Bravanský, Filip Trhlík +1
We present the Verifee Dataset: a novel dataset of news articles with fine-grained trustworthiness annotations. We develop a detailed methodology that assesses the texts based on t…