2 citations · 2 across the 6 of their papers we have counts for
10 papers · 1 filter
GAMBIT: A Three-Mode Benchmark for Adversarial Robustness in Multi-Agent LLM Collectives
Alexandre Le Mercier, Chris Develder, Thomas Demeester
In multi-agent systems (MAS), a single deceptive agent can nullify all gains of an agentic AI collective and evade deployed defenses. However, existing adversarial studies on MAS t…
CLASP: Defending Hybrid Large Language Models Against Hidden State Poisoning Attacks
Alexandre Le Mercier, Thomas Demeester, Chris Develder
State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining competitive performan…
Hidden State Poisoning Attacks against Mamba-based Language Models
Alexandre Le Mercier, Chris Develder, Thomas Demeester
State space models (SSMs) like Mamba offer efficient alternatives to Transformer-based language models, with linear time complexity. Yet, their adversarial robustness remains criti…
WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain
Matthias De Lange, Warre Veys, Federico Retyk +16
Today's evolving labor markets rely increasingly on recommender systems for hiring, talent management, and workforce analytics, with natural language processing (NLP) capabilities…
A Customer Journey in the Land of Oz: Leveraging the Wizard of Oz Technique to Model Emotions in Customer Service Interactions
Sofie Labat, Thomas Demeester, Véronique Hoste
Emotion-aware customer service needs in-domain conversational data, rich annotations, and predictive capabilities, but existing resources for emotion recognition are often out-of-d…
Single- vs. Dual-Prompt Dialogue Generation with LLMs for Job Interviews in Human Resources
Joachim De Baer, A. Seza DoÄruöz, Thomas Demeester +1
Optimizing language models for use in conversational agents requires large quantities of example dialogues. Increasingly, these dialogues are synthetically generated by using power…