About Me

I’m a research scientist at Cohere for AI, where I conduct research on large language models, centered around multilinguality, reinforcement learning, and evaluation. Previously I worked at Google Research, Montreal, with a focus on machine translation. Very broadly speaking, I am interested in the intersection of natural language processing (NLP) and machine learning, especially where multiple languages come into play.

In my PhD (Heidelberg University, Germany) I investigated how reinforcement learning algorithms can be used to turn weak supervision signals from users into meaningful updates for a machine translation system (=RLHF before it was cool). More recently, I have focused on improving multilinguality, language diversity and data quality in machine translation and related applications.

My long-term goal for NLP research is to make it more accessible, along multiple dimensions:

1. Underresourced NLP: Foster research for underresourced languages and by underrepresented groups, such that not only English-speaking users can benefit from the progress we’re making in NLP.
2. Novices: Reduce the entry burdens (in terms of coding and research practices) for novices in the field, especially for new students or researchers from other related areas.
3. Science outreach: Get the general public, especially highschool students, more interested in research in machine learning to grow a better understanding of what our current methods look like and where their limitations are.

News

• Feb 2024: Giving a guest lecture on the Aya project in Siva Reddy’s class on Natural Language Understanding with Deep Learning / Computational Semantics at McGill. Slides available upon request.
• Feb 2024: New preprint about RLHF: “Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs”. This work led by Cohere for AI scholar Arash Ahmadian scrutinizes the popular PPO algorithm for RLHF in LLMs, and presents effective but simpler alternatives that are grounded in the classic (and basic!) REINFORCE algorithm. Throwback to my PhD topic :)
• Feb 2024: Project Aya released its model and data! Detailed documentation can be found in the preprints (model, data). This work is the result of a massive open-science collaboration, aiming to build a massively multilingual instruction fine-tuned large language model. My own contributions focus on testing the model for bias, toxicity and harm, and on conducting and comparing human and automatic evaluation of open-ended generation quality.
• Oct 2023: Joining the Cohere for AI lab after one year of parental leave.

Publications

Google scholar

Code

• Joey NMT

Resources

• HumanMT: Human feedback for MT from different interfaces
• Blog post on RL for NMT
• Blog post on Joey NMT

Contact

Email: <lowercase first + last name>.@cohere.com