Natural Language Processing
Much of the information that can help transform enterprises is locked away in text, like documents, tables, and charts. We’re building advanced AI systems that can parse vast bodies of text to help unlock that data, but also ones flexible enough to be applied to any language problem.
Our work
What is AI alignment?
ExplainerKim Martineau- AI
- Automated AI
- Fairness, Accountability, Transparency
- Foundation Models
- Natural Language Processing
AI transformers shed light on the brain’s mysterious astrocytes
ResearchKim Martineau- AI
- Foundation Models
- Life Sciences
- Machine Learning
- Natural Language Processing
- Science
What is retrieval-augmented generation?
ExplainerKim Martineau- AI
- Explainable AI
- Generative AI
- Natural Language Processing
- Trustworthy Generation
How Intel oneAPI tools are accelerating IBM's Watson Natural Language Processing Library
Technical noteStephanie Kuan, Deb Bharadwaj, Preethi Venkatesh, Shankar Ratneshwaran, and Waleed Khan- Natural Language Processing
What is prompt-tuning?
NewsKim Martineau- AI
- Computer Vision
- Fairness, Accountability, Transparency
- Foundation Models
- Machine Learning
- Natural Language Processing
IBM and NASA team up to spur new discoveries about our planet
NewsKim Martineau3 minute read- Accelerated Discovery
- AI
- Climate
- Computer Vision
- Foundation Models
- Hybrid Cloud Platform
- Natural Language Processing
- Scaling AI
- See more of our work on Natural Language Processing
Publications
- Shuli Jiang
- Swanand Ravindra Kadhe
- et al.
- 2023
- NeurIPS 2023
- Hongyi Wang
- Felipe Maia Polo
- et al.
- 2023
- NeurIPS 2023
- Tal Shnitzer
- Anthony Ou
- et al.
- 2023
- NeurIPS 2023
- Jehanzeb Mirza
- Leonid Karlinsky
- et al.
- 2023
- NeurIPS 2023
- Xiaomeng Hu
- Pin-Yu Chen
- et al.
- 2023
- NeurIPS 2023
- Prateek Yadav
- Derek Tam
- et al.
- 2023
- NeurIPS 2023
Tools + code
Transition-based AMR Parser
Transition-based parser for Abstract Meaning Representation (AMR) in Pytorch.
View project →sIB: sequential Information Bottleneck
Implementation of sequential Information Bottleneck (sIB) in Python and in C++
View project →Low-Resource Text Classification Framework
Research framework for low resource text classification that allows the user to experiment with classification models and active learning strategies on a large number of sentence classification datasets, and to simulate real-world scenarios. The framework is easily expandable to new classification models, active learning strategies and datasets.
View project →Project Debater for Academic Use
The technologies underlying Project Debater available as cloud services. Includes core natural language understanding capabilities, argument mining, and narrative generation.
View project →