23 citations · 38 across the 9 of their papers we have counts for
9 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Recent model editing techniques promise to mitigate the problem of memorizing false or outdated associations during LLM training.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…
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
Jason Hoelscher-Obermaier, Edward Stevinson, Valentin Stauber +4
The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors fo…
Domain-adaptation of spherical embeddings
Mihalis Gongolidis, Jeremy Minton, Ronin Wu +3
Domain adaptation of embedding models, updating a generic embedding to the language of a specific domain, is a proven technique for domains that have insufficient data to train an…
A search of the Orion spur for continuous gravitational waves using a "loosely coherent" algorithm on data from LIGO interferometers
J. Aasi, B. P. Abbott, R. Abbott +920
We report results of a wideband search for periodic gravitational waves from isolated neutron stars within the Orion spur towards both the inner and outer regions of our Galaxy. As…