Bernd Ludwig


2024

pdf bib
Linguistic Obfuscation Attacks and Large Language Model Uncertainty
Sebastian Steindl | Ulrich Schäfer | Bernd Ludwig | Patrick Levi
Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)

Large Language Models (LLMs) have taken the research field of Natural Language Processing by storm. Researchers are not only investigating their capabilities and possible applications, but also their weaknesses and how they may be exploited.This has resulted in various attacks and “jailbreaking” approaches that have gained large interest within the community.The vulnerability of LLMs to certain types of input may pose major risks regarding the real-world usage of LLMs in productive operations.We therefore investigate the relationship between a LLM’s uncertainty and its vulnerability to jailbreaking attacks.To this end, we focus on a probabilistic point of view of uncertainty and employ a state-of-the art open-source LLM.We investigate an attack that is based on linguistic obfuscation.Our results indicate that the model is subject to a higher level of uncertainty when confronted with manipulated prompts that aim to evade security mechanisms.This study lays the foundation for future research into the link between model uncertainty and its vulnerability to jailbreaks.

2023

pdf bib
Controlled Data Augmentation for Training Task-Oriented Dialog Systems with Low Resource Data
Sebastian Steindl | Ulrich Schäfer | Bernd Ludwig
Proceedings of the 2nd Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

Modern dialog systems rely on Deep Learning to train transformer-based model architectures. These notoriously rely on large amounts of training data. However, the collection of conversational data is often a tedious and costly process. This is especially true for Task-Oriented Dialogs, where the system ought to help the user achieve specific tasks, such as making reservations. We investigate a controlled strategy for dialog synthesis. Our method generates utterances based on dialog annotations in a sequence-to-sequence manner. Besides exploring the viability of the approach itself, we also explore the effect of constrained beam search on the generation capabilities. Moreover, we analyze the effectiveness of the proposed method as a data augmentation by studying the impact the synthetic dialogs have on training dialog systems. We perform the experiments in multiple settings, simulating various amounts of ground-truth data. Our work shows that a controlled generation approach is a viable method to synthesize Task-Oriented Dialogs, that can in turn be used to train dialog systems. We were able to improve this process by utilizing constrained beam search.

2021

pdf bib
UR@NLP_A_Team @ GermEval 2021: Ensemble-based Classification of Toxic, Engaging and Fact-Claiming Comments
Kwabena Odame Akomeah | Udo Kruschwitz | Bernd Ludwig
Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments

In this paper, we report on our approach to addressing the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments for the German language. We submitted three runs for each subtask based on ensembles of three models each using contextual embeddings from pre-trained language models using SVM and neural-network-based classifiers. We include language-specific as well as language-agnostic language models – both with and without fine-tuning. We observe that for the runs we submitted that the SVM models overfitted the training data and this affected the aggregation method (simple majority voting) of the ensembles. The model records a lower performance on the test set than on the training set. Exploring the issue of overfitting we uncovered that due to a bug in the pipeline the runs we submitted had not been trained on the full set but only on a small training set. Therefore in this paper we also include the results we get when trained on the full training set which demonstrate the power of ensembles.

2006

pdf bib
Tracing Actions Helps in Understanding Interactions
Bernd Ludwig
Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue