Background image for aesthetic purposes

Cohere For AI

Cohere For AI is Cohere's research lab that seeks to solve complex machine learning problems. We support fundamental research that explores the unknown, and are focused on creating more points of entry into machine learning research.

Background image for aesthetic purposes

Fundamental research lab

We work at the frontier of AI progress with the goal of solving cutting edge scientific problems. We see contributions to traditional conferences and publications in journals as an important part of our work, but also support efforts that go “beyond the research paper” and encourage scientific communication through different mediums. We drive the creation of new research spaces and breakthroughs that changes where, how and by whom research is done. We believe that technology is powerful, and empowering different perspectives ensures responsible innovation.

Open Science Initiative

We’re not just another research group. We are a hybrid lab with both a dedicated research staff and support for open science initiatives. We collaborate openly with independent researchers all over the world to conduct top-tier ML research.


Our open science research community is a space where researchers, engineers, linguists, social scientists, and lifelong learners connect and collaborate with each other. We come together from over 100 countries around the world and support large and small scale research collaborations.

Our models

Featured image for article

State of the Art, Accessible Research LLM

Aya 23 - 8B

Featured image for article

State of the Art Research LLM

Aya 23 - 35B

Featured image for article

Massively Multilingual Research LLM

Aya

Featured image for article

MODEL WEIGHTS FOR DEMOCRATIZING RESEARCH ACCESS

C4AI Command R - 104B

Featured image for article

MODEL WEIGHTS FOR DEMOCRATIZING RESEARCH ACCESS

C4AI Command R - 35B

Our papers

Featured image for article

Nexus: Specialization meets Adaptability for Efficiently Training Mixture of Experts

In our latest work, we ask “How to make Mixture-of-Expert models more efficient, specialized, and adaptable at once?” We introduce Nexus, which enables specialization and adaptability within the efficient upcycling framework.

Featured image for article

Multilingual Arbitrage: Optimizing Data Pools to Accelerate Multilingual Progress

Can you surpass individual model performance by sampling parts of the distribution strategically from a pool of models? We introduce “multilingual arbitrage” to describe capitalizing on performance variations to produce large gains in performance.

Featured image for article

To Code, or Not To Code? Exploring Impact of Code in Pre-training

Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. e ask "what is the impact of code data used in pre-training on a large variety of downstream tasks beyond code generation".

Featured image for article

Consent in Crisis: The Rapid Decline of the AI Data Commons

General-purpose AI systems are built on massive swathes of public web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge, we conduct the first, large-scale, longitudinal audit of the consent protocols for the web domains underlying AI training corpora.

Featured image for article

How Does Quantization Affect Multilingual LLMs?

Quantization techniques are widely used to improve inference speed and deployment of large language models. While a wide body of work examines the impact of quantized LLMs on English tasks, none have examined the effect of quantization across languages.

Featured image for article

RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs

We introduce a novel, scalable method for generating high-quality multilingual feedback data to balance data coverage. We establish the benefits of cross-lingual transfer and increased dataset size in preference training.

Featured image for article

LLM See, LLM Do: Guiding Data Generation to Target Non-Differentiable Objectives

Our work exhaustively characterizes the impact of passive inheritance of model properties by systematically studying the consequences of synthetic data integration.

Featured image for article

The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm

We explore the viability of different alignment approaches when balancing dual objectives: addressing and optimizing for a non-homogeneous set of languages and cultural preferences while minimizing both global and local harms.

Featured image for article

Self-Improving Robust Preference Optimization

The key idea of SRPO is to cast the problem of learning from human preferences as a self-improvement process, which can be mathematically expressed in terms of a min-max objective that aims at joint optimization of self-improvement policy and the generative policy in an adversarial fashion.

Our programs

Advancing the NLP space through our programs.

ACCELERATING MULTILINGUAL AI THROUGH OPEN SCIENCE

Introducing Aya

About

Aya is a state-of-the-art multilingual open-source research model and dataset covering 101 languages. Developed by 3000 independent researchers from 119 countries, Aya aims to fast-track multilingual AI progress.

Exploring the unknown together

Scholars program

About

Our Scholars Program provides the opportunity to work alongside some of the best research and engineering experts in the world. We have created an open and supportive environment that provides an alternative point of entry into machine learning research.

academic support

Research grant

Benefits

Cohere For AI research grants are designed to support academic partners who are conducting research with the goal of releasing a peer-reviewed scientific artifact. Our program provides academic partners, developers, researchers, and other members of our community with subsidized access to the Cohere API.

Past events and videos

Research is inherently a human endeavor, and our event series provide insights from beginning to breakthrough.

Featured image for article

Video

Cheng Soon Ong: Why you should learn Mathematics for ML

Featured image for article

Video

Using spatial technologies in search efforts of disappearances in Mexico

Featured image for article

Video

Fireside Chat: Minjoon Seo

Featured image for article

Video

Q-Diffusion: Quantizing Diffusion Models

Featured image for article

Video

Fireside Chat: Mirella Lapata

Featured image for article

Video

Sigmoid Loss for Language Image Pre-Training

Meet our research team

Our staff brings together machine learning experts to contribute to progress in machine learning through fundamental research. We are committed to open collaboration, and empowering more points of entry into machine learning research through our scholars program.

head, Cohere for ai

Sara hooker

head, Cohere for ai

Senior Research Scientist

Marzieh Fadaee

Senior Research Scientist

SENIOR RESEARCH SCIENTIST

Julia Kreutzer

SENIOR RESEARCH SCIENTIST

Research Scientist

Ahmet Üstün

Research Scientist

Research Scientist

Beyza Ermis

Research Scientist

Operations and Community Lead

Madeline Smith

Operations and Community Lead

Policy & Responsible AI Lead

Aidan Peppin

Policy & Responsible AI Lead

Operations Associate

Brittawnya Prince

Operations Associate

Operations Associate

Arielle Salman Bailey

Operations Associate

Research Engineer

Saurabh Dash

Research Engineer

Research Engineer

Daniel D'souza

Research Engineer

Open Science Research Engineer

Alejandro Salamanca

Open Science Research Engineer

Open Science MLE

Shivalika Singh

Open Science MLE

Research Scholar

Aakanksha

Research Scholar

Research Scholar

Viraat Aryabumi

Research Scholar

Research Scholar

John Dang

Research Scholar

Research Scholar

Oliver Nan

Research Scholar

Research Scholar

Luísa Shimabucoro

Research Scholar

Research Scholar

Arash Ahmadian Dehkordi

Research Scholar

Frequently Asked Questions

  • What’s C4AI’s origin story?
    • In 2017, a team of friends, classmates, and engineers started a distributed research collaboration, with a focus on creating a medium for early-career AI enthusiasts to engage with experienced researchers – they called it “for.ai.” Two of those co-founding members, Aidan Gomez and Ivan Zhang, later went on to co-found Cohere, and many of the original members went on to do exciting things (pursuing PhDs, working at industry and academic labs).


      At the time, For AI was one of the first community-driven research groups to support independent researchers around the world. Today, Cohere is proud to reintroduce For AI as Cohere For AI, a dedicated research lab and community for exploring the unknown, together. Watch the C4AI history video here.

  • Do you charge for your educational programs or community membership?
    • We do not charge for participating in any of our programs, and are committed to supporting educational outreach programs, which include compute resources and infrastructure needed to participate in machine learning research.

  • are you hiring for research positions or interns?
    • Our full list of positions are listed here.

  • How can I stay in touch?
  • What is Aya?
    • Aya is a state-of-the-art, open source, massively multilingual research LLM covering 101 languages – including more than 50 previously underserved languages. Learn more here.

Background image for aesthetic purposes

Join our open science community

Collaborate with researchers, engineers, linguists, social scientists, and lifelong learners from 100+ countries on top-tier ML research.