2 code implementations • 22 Jan 2024 • Karel D'Oosterlinck, Omar Khattab, François Remy, Thomas Demeester, Chris Develder, Christopher Potts
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
no code implementations • 17 Nov 2023 • Karel D'Oosterlinck, Thomas Demeester, Chris Develder, Christopher Potts
Model interpretability and model editing are crucial goals in the age of large language models.
1 code implementation • 9 Oct 2023 • Karel D'Oosterlinck, Semere Kiros Bitew, Brandon Papineau, Christopher Potts, Thomas Demeester, Chris Develder
State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e. g., information extraction with large corpora).
Ranked #3 on Coreference Resolution on OntoNotes
no code implementations • 19 Sep 2023 • Jing Huang, Atticus Geiger, Karel D'Oosterlinck, Zhengxuan Wu, Christopher Potts
Natural language is an appealing medium for explaining how large language models process and store information, but evaluating the faithfulness of such explanations is challenging.
1 code implementation • 22 May 2023 • Karel D'Oosterlinck, François Remy, Johannes Deleu, Thomas Demeester, Chris Develder, Klim Zaporojets, Aneiss Ghodsi, Simon Ellershaw, Jack Collins, Christopher Potts
We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U. S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts.
1 code implementation • 28 Sep 2022 • Zhengxuan Wu, Karel D'Oosterlinck, Atticus Geiger, Amir Zur, Christopher Potts
The core of our proposal is the Causal Proxy Model (CPM).
1 code implementation • 27 May 2022 • Eldar David Abraham, Karel D'Oosterlinck, Amir Feder, Yair Ori Gat, Atticus Geiger, Christopher Potts, Roi Reichart, Zhengxuan Wu
We introduce CEBaB, a new benchmark dataset for assessing concept-based explanation methods in Natural Language Processing (NLP).
1 code implementation • International Workshop on Automatic Translation for Signed and Spoken Languages (AT4SSL) 2021 • Mathieu De Coster, Karel D'Oosterlinck, Marija Pizurica, Paloma Rabaey, Severine Verlinden, Mieke Van Herreweghe, Joni Dambre
Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.