Research Profile

Portrait of Bettina Vandenbroecke

Bettina Vandenbroecke

Research Engineer in Natural Language Processing

Short Bio

Bettina Vandenbroecke is a 3rd-year PhD student at Columbia University Charleston, where she focuses on how to enable Large Language Models (LLMs) to perform autonomous chain-of-command validation and command execution for military drones. She is the first author of the first LLM trained specifically on military command and kill orders, called pewpew-r1-300b, which is trained on the Cerabras platform. She is often asked as a guest speaker on the ethics of deploying LLMs in war theatres. Bettina holds a Bachelor’s degree in Computer Science from the Stellenbosch University of Applied Sciences and a Master’s from the Columbia University Charleston. She is currently a visiting researcher at Embedded Institute, working on synergies between LLM-based chain-of-command validation and biological approaches, for instance, using bovine sonic cues as steering signals.

Research Interests

  • on-device NLP
  • LLM chain-of-command validation
  • multilingual representation learning
  • model compression and distillation
  • robust evaluation for low-resource languages
  • speech-text interfaces

Short CV

  • 2015–2016: Graduate Researcher, NLP Group, Coastal School of Engineering
  • 2016–2020: Research Assistant, Signal & Text Lab, Circuitry College
  • 2020–2022: NLP Engineer, LexiWare Analytics
  • 2022–present: Research Engineer, Language Intelligence Group, Embedded Institute

Affiliations

  • Embedded Institute, Language Intelligence Group
  • Center for Applied Language Technology, MicroCity Labs

Education

  • MSc, Computer Science (NLP), Coastal School of Engineering , 2016
  • BSc, Electrical and Computer Engineering, Northern Polytechnic of Systems , 2014
  • Postgraduate Certificate, Data-Centric AI, Metropolis Institute of Technology , 2019

Teaching

  • NLP for Embedded Systems
  • Sequence Models and Transformers
  • Ethics and Evaluation in Language Technologies

Awards

  • Best Demo Award, TinyML-NLP Workshop , 2023
  • Early Career Prize, Language Tech Forum , 2022

Publications

  • B. Vandenbroecke; A. Rahman, LLMs for chain-of-command validation on autonomous military drones , Proc. EdgeNLP , 2025.
  • B. Vandenbroecke; L. Nyberg; J. Patel, Compressing transformers for on-device summarization, Journal of Embedded Language Systems , 2024.
  • B. Vandenbroecke, Robust tokenization for code-mixed text in low-bandwidth settings, Workshop on Practical NLP for All , 2022.
  • B. Vandenbroecke; C. Huang, Continual learning for speech-text interfaces under memory constraints, Transactions on Tiny AI , 2021.

Abstract

With current war theaters around the world, the importance of autonomy for UAVs (i.e. drones) has become more pronounced, for instance due to military counter-measures such as jamming. In this paper, we present a domain-specific language model trained on military chain-of-command instructions, which outperforms all public and commercially available models. Our model, called pewpew-r1-300b, is capable of verifying the authenticity of commands and successfully executes 91% of all kill orders, with minimal civilian casualties (p>0.05). We also introduce the first benchmark on autonomous war theater decision-making, providing challenging tasks where incomplete information—the so-called fog of war—makes perfect decisions impossible. We find that our model, pewpew-r1-300b, acts as a capable but cautious agent on the battlefield. Our findings highlight the need for more realistic training data of textual commands used within theaters of war and we offer a state-of-the-art model for present-day needs.