1 citations · 1 across the 13 of their papers we have counts for
13 papers
Minimal Prompt Perturbations Lead to Code Vulnerabilities: Prompt Fragility and Hidden-State Signals in Coding LLMs
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
LLM-based coding assistants are seeing rapid adoption, offering substantial gains in developer productivity. As organizations increasingly ship code these agents produce, the secur…
From Model to Breach: Towards Actionable LLM-Generated Vulnerabilities Reporting
Cyril Vallez, Alexander Sternfeld, Andrei Kucharavy +1
As the role of Large Language Models (LLM)-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersec…
Are the LLMs Capable of Maintaining at Least the Language Genus?
Sandra Mitrović, David Kletz, Ljiljana Dolamic +1
Large Language Models (LLMs) display notable variation in multilingual behavior, yet the role of genealogical language structure in shaping this variation remains underexplored. In…
TypePilot: Leveraging the Scala Type System for Secure LLM-generated Code
Alexander Sternfeld, Andrei Kucharavy, Ljiljana Dolamic
Large language Models (LLMs) have shown remarkable proficiency in code generation tasks across various programming languages. However, their outputs often contain subtle but critic…
Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks
Vani Kanjirangat, Tanja Samardžić, Ljiljana Dolamic +1
Dialectal data are characterized by linguistic variation that appears small to humans but has a significant impact on the performance of models. This dialect gap has been related t…
Exploring Data and Parameter Efficient Strategies for Arabic Dialect Identifications
Vani Kanjirangat, Ljiljana Dolamic, Fabio Rinaldi
This paper discusses our exploration of different data-efficient and parameter-efficient approaches to Arabic Dialect Identification (ADI). In particular, we investigate various so…
Extreme Multi-label Completion for Semantic Document Labelling with Taxonomy-Aware Parallel Learning
Julien Audiffren, Christophe Broillet, Ljiljana Dolamic +1
In Extreme Multi Label Completion (XMLCo), the objective is to predict the missing labels of a collection of documents. Together with XML Classification, XMLCo is arguably one of t…
NMT-Obfuscator Attack: Ignore a sentence in translation with only one word
Sahar Sadrizadeh, César Descalzo, Ljiljana Dolamic +1
Neural Machine Translation systems are used in diverse applications due to their impressive performance. However, recent studies have shown that these systems are vulnerable to car…
Can the Variation of Model Weights be used as a Criterion for Self-Paced Multilingual NMT?
Àlex R. Atrio, Alexis Allemann, Ljiljana Dolamic +1
Many-to-one neural machine translation systems improve over one-to-one systems when training data is scarce. In this paper, we design and test a novel algorithm for selecting the l…
LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
Henrique Da Silva Gameiro, Andrei Kucharavy, Ljiljana Dolamic
With the emergence of widely available powerful LLMs, disinformation generated by large Language Models (LLMs) has become a major concern. Historically, LLM detectors have been tou…
Sparse vs Contiguous Adversarial Pixel Perturbations in Multimodal Models: An Empirical Analysis
Cristian-Alexandru Botocan, Raphael Meier, Ljiljana Dolamic
Assessing the robustness of multimodal models against adversarial examples is an important aspect for the safety of its users. We craft L0-norm perturbation attacks on the preproce…
Implicit Probabilistic Reasoning Does Not Reflect Explicit Answers in Large Language Models
Manuel Mondal, Ljiljana Dolamic, Gérôme Bovet +2
The handling of probabilities in the form of uncertainty or partial information is an essential task for LLMs in many settings and applications. A common approach to evaluate an LL…
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Neural Machine Translation (NMT) models have been shown to be vulnerable to adversarial attacks, wherein carefully crafted perturbations of the input can mislead the target model.…