10 citations · 15 across the 8 of their papers we have counts for
8 papers
Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Language Models
Philip Quirke, Narmeen Oozeer, Chaithanya Bandi +8
This position paper argues that the prevailing trajectory toward ever larger, more expensive generalist foundation models controlled by a handful of companies limits innovation and…
Multi-Agent Risks from Advanced AI
Lewis Hammond, Alan Chan, Jesse Clifton +41
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These s…
Multi-Agent Security Tax: Trading Off Security and Collaboration Capabilities in Multi-Agent Systems
Pierre Peigne-Lefebvre, Mikolaj Kniejski, Filip Sondej +4
As AI agents are increasingly adopted to collaborate on complex objectives, ensuring the security of autonomous multi-agent systems becomes crucial. We develop simulations of agent…
Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest
Emily Groves, Minhong Wang, Yusuf Abdulle +4
Automated knowledge curation for biomedical ontologies is key to ensure that they remain comprehensive, high-quality and up-to-date. In the era of foundational language models, thi…
Self-Consistency of Large Language Models under Ambiguity
Henning Bartsch, Ole Jorgensen, Domenic Rosati +2
Large language models (LLMs) that do not give consistent answers across contexts are problematic when used for tasks with expectations of consistency, e.g., question-answering, exp…
Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark
Jason Hoelscher-Obermaier, Julia Persson, Esben Kran +2
Recent model editing techniques promise to mitigate the problem of memorizing false or outdated associations during LLM training. However, we show that these techniques can introdu…