4 citations · 11 across the 9 of their papers we have counts for
10 papers
Translation as a Decision Space: A Multi-Agent Perspective on Low-Resource Dialect Generation
Hasan Alkhder, Mohammad Abboush, Igor Tchappi +2
Neural machine translation (NMT) systems typically produce a single output per input, obscuring the alternative decision trajectories implicitly available within multilingual decod…
A Lay User Explainable Food Recommendation System Based on Hybrid Feature Importance Extraction and Large Language Models
Melissa Tessa, Diderot D. Cidjeu, Rachele Carli +4
Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more ela…
When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models
Afshin Khadangi, Hanna Marxen, Amir Sartipi +2
Frontier language models increasingly participate in conversations about distress and mental health, yet the mechanisms that generate anthropomorphic self narratives remain unclear…
Efficient Differentially Private Fine-Tuning of LLMs via Reinforcement Learning
Afshin Khadangi, Amir Sartipi, Igor Tchappi +2
The tension between data privacy and model utility has become the defining bottleneck for the practical deployment of large language models (LLMs) trained on sensitive corpora incl…
Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting
Timothée Hornek Amir Sartipi, Igor Tchappi, Gilbert Fridgen
Accurate electricity price forecasting (EPF) is crucial for effective decision-making in power trading on the spot market. While recent advances in generative artificial intelligen…
KG-HTC: Integrating Knowledge Graphs into LLMs for Effective Zero-shot Hierarchical Text Classification
Qianbo Zang, Christophe Zgrzendek, Igor Tchappi +2
Hierarchical Text Classification (HTC) involves assigning documents to labels organized within a taxonomy. Most previous research on HTC has focused on supervised methods. However,…
Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models
Afshin Khadangi, Amir Sartipi, Igor Tchappi +1
Large language models (LLMs) often produce inaccurate or misleading content-hallucinations. To address this challenge, we introduce Noise-Augmented Fine-Tuning (NoiseFiT), a novel…
Towards Effective E-Participation of Citizens in the European Union: The Development of AskThePublic
Nils Messerschmidt, Kilian Sprenkamp, Amir Sartipi +4
E-participation platforms are an important asset for governments in increasing trust and fostering democratic societies. By engaging public and private institutions and individuals…
CognArtive: Large Language Models for Automating Art Analysis and Decoding Aesthetic Elements
Afshin Khadangi, Amir Sartipi, Igor Tchappi +1
Art, as a universal language, can be interpreted in diverse ways, with artworks embodying profound meanings and nuances. The advent of Large Language Models (LLMs) and the availabi…
Towards Explainability for a Civilian UAV Fleet Management using an Agent-based Approach
Yazan Mualla, Amro Najjar, Timotheus Kampik +3
This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the huma…