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
BeeAI now has multiple agents, and a standardized way for them to talk
ResearchKim MartineauIBM’s Mikhail Yurochkin wants to make AI’s “cool” factor tangible
ResearchKim MartineauIBM Granite now has eyes
ResearchKim MartineauA benchmark for evaluating conversational RAG
ResearchKim MartineauCan a personalized AI be more useful?
NewsKim MartineauIBM Granite has new experimental features for developers to test
NewsKim Martineau- See more of our work on Natural Language Processing
Publications
Designing and implementing LLM guardrails components in production environments
- Mateus Do Amor Devino Pereira
- Evaline Ju
- et al.
- 2025
- CAIN 2025
Diagnosing and Prioritizing Issues in Automated Order-Taking Systems: A Machine-Assisted Error Discovery Approach
- Maeda Hanafi
- Frederick Reiss
- et al.
- 2025
- CHI 2025
Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining
- Daouda Sow
- Herbert Woisetschläger
- et al.
- 2025
- ICLR 2025
Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons
- Shahaf Bassan
- Ron Eliav
- et al.
- 2025
- ICLR 2025
Text-Guided Few-Shot Semantic Segmentation with Training-Free Multimodal Feature Matching
- Guillaume Buthmann
- Tomoya Sakai
- et al.
- 2025
- ICASSP 2025
Foundation models for materials discovery – current state and future directions
- Edward Pyzer-knapp
- Matteo Manica
- et al.
- 2025
- npj Computational Materials