Pretrained LLMs have demonstrated impressive abilities, but it is hard to understand how they work or how well they will generalise to a new domain. Idiap researchers are developing a model of how information is represented inside LLMs. By identifying and removing unreliable information, this model can improve generalisation to new domains, without the need or any additional training.
Natural Language Understanding
In our group, we model summarization, abstraction (textual entailment), machine translation, knowledge extraction, syntactic structure, and lexical semantics, among other natural language processing (NLP) problems. We develop deep learning models of the discovery and prediction of entities and their relations at multiple levels of representation for multiple tasks.
Group News
Bayesian Language Understanding with Nonparametric Variational Transformers - Talk Jan 29 2024
As artificial intelligence technologies reach everyday new performances, their energy cost is also increasing significantly. Idiap researchers are proposing a novel approach to address this challenge during a period of rising energy costs.
Idiap Research Institute and the School of Engineering at EPFL invite applications for the directorship of Idiap. The successful candidate will also hold a faculty position as full professor at EPFL School of Engineering.
Demonstrating a significant improvements over the previous state-of-the-art results, James Henderson and Alireza Mohammadshahi of the Natural language understanding group propose a novel approach. Their work was presented during the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL) on April 21, 2021.
Group Job Openings
Our group is regularly posting job openings ranging from internships to researcher positions. To check the opportunities currently available or to submit a speculative applications use the link below.Our group is regularly posting job openings ranging from internships to researcher positions. To check the opportunities currently available or to submit a speculative applications use the link below.
Current Group Members
The group is led by James Henderson.
HENDERSON, James (Brinton)
(Senior Research Scientist)
- website
FEHR, Fabio (James)
(PhD Student / Research Assistant)
- website
EL ZEIN, Dina
(PhD Student / Research Assistant)
- website
COMAN, Andrei (Catalin)
(PhD Student / Research Assistant)
- website
SHIRAKAMI, Haruki
(PhD Student / Research Assistant)
- website
Alumni
Please note that this list is not exhaustive.
- BEHJATI, Melika
- BHATT, Chidansh
- GASNIER, Catherine
- HABIBI, Maryam
- HAJLAOUI, Najeh
- HONNET, Pierre-Edouard
- KARIMI MAHABADI, Rabeeh
- LE, Quoc Anh
- LISON, Pierre
- LIYANAPATHIRANA, Jeevanthi Uthpala
- LOAICIGA, Sharid
- LUONG, Ngoc-Quang
- MAHDABI, Parvaz
- MATENA, Lukas
- MEYER, Braida (Regula)
- MEYER, Thomas
- MICULICICH, Lesly (Sadiht)
- MOHAMMADSHAHI, Alireza
- PAPPAS, Nikolaos
- PATEL, Kumar
- POPESCU-BELIS, Andrei
- PU, Xiao
- REKABSAZ, Navid
- YAZDANI, Majid
Active Research Grants
Past Research Grants
- AROLES - Automatic Recommendation of Lectures and Snipets
- COMTIS - Improving the coherence of machine translation output by modeling intersentential relations
- DOMAT - On-demand Knowledge for Document-level Machine Translation
- EVOLANG - Evolving Language
- FAVEO - Accelerating online information discovery through context-driven and behaviour-based personalization of search
- HYBRID - Hybrid Recommender System
- INTREPID - Automated interpretation of political and economic policy documents: Machine learning using semantic and syntactic information
- LAOS - Learning Representations of Abstraction in Text
- MODERN - Modeling discourse entities and relations for coherent machine translation
- REMUS - REMUS: Re-ranking Multiple Search Results for Just-in-Time Document Recommendation
- SARAL - Summarization and domain-Adaptive Retrieval of Information Across Languages