1 citations · 2 across the 7 of their papers we have counts for
6 papers · 1 filter
Steerability of Instrumental-Convergence Tendencies in LLMs
Jakub Hoscilowicz
We examine two properties of AI systems: capability (what a system can do) and steerability (how reliably one can shift behavior toward intended outcomes). A central question is wh…
Adversarial Confusion Attack: Disrupting Multimodal Large Language Models
Jakub Hoscilowicz, Artur Janicki
We introduce the Adversarial Confusion Attack, a new class of threats against multimodal large language models (MLLMs). Unlike jailbreaks or targeted misclassification, the goal is…
Large Language Models as Carriers of Hidden Messages
Jakub Hoscilowicz, Pawel Popiolek, Jan Rudkowski +2
Simple fine-tuning can embed hidden text into large language models (LLMs), which is revealed only when triggered by a specific query. Applications include LLM fingerprinting, wher…
Large Language Models for Expansion of Spoken Language Understanding Systems to New Languages
Jakub Hoscilowicz, Pawel Pawlowski, Marcin Skorupa +2
Spoken Language Understanding (SLU) models are a core component of voice assistants (VA), such as Alexa, Bixby, and Google Assistant. In this paper, we introduce a pipeline designe…
Non-Linear Inference Time Intervention: Improving LLM Truthfulness
Jakub Hoscilowicz, Adam Wiacek, Jan Chojnacki +4
In this work, we explore LLM's internal representation space to identify attention heads that contain the most truthful and accurate information. We further developed the Inference…
Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?
Jakub Hościłowicz, Marcin Sowański, Piotr Czubowski +1
In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model. Our ul…