281 citations · 902 across the 36 of their papers we have counts for
9 papers · 1 filter
ConceptX: A Framework for Latent Concept Analysis
Firoj Alam, Fahim Dalvi, Nadir Durrani +3
The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework fo…
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Enes Altinisik, Hassan Sajjad, Husrev Taha Sencar +2
Adversarial training is widely acknowledged as the most effective defense against adversarial attacks. However, it is also well established that achieving both robustness and gener…
On the Transformation of Latent Space in Fine-Tuned NLP Models
Nadir Durrani, Hassan Sajjad, Fahim Dalvi +1
We study the evolution of latent space in fine-tuned NLP models. Different from the commonly used probing-framework, we opt for an unsupervised method to analyze representations. M…
Post-hoc analysis of Arabic transformer models
Ahmed Abdelali, Nadir Durrani, Fahim Dalvi +1
Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects…
Analyzing Encoded Concepts in Transformer Language Models
Hassan Sajjad, Nadir Durrani, Fahim Dalvi +3
We propose a novel framework ConceptX, to analyze how latent concepts are encoded in representations learned within pre-trained language models. It uses clustering to discover the…
Discovering Salient Neurons in Deep NLP Models
Nadir Durrani, Fahim Dalvi, Hassan Sajjad
While a lot of work has been done in understanding representations learned within deep NLP models and what knowledge they capture, little attention has been paid towards individual…
Discovering Latent Concepts Learned in BERT
Fahim Dalvi, Abdul Rafae Khan, Firoj Alam +3
A large number of studies that analyze deep neural network models and their ability to encode various linguistic and non-linguistic concepts provide an interpretation of the inner…
Probing for Constituency Structure in Neural Language Models
David Arps, Younes Samih, Laura Kallmeyer +1
In this paper, we investigate to which extent contextual neural language models (LMs) implicitly learn syntactic structure. More concretely, we focus on constituent structure as re…
Interpreting Arabic Transformer Models
Ahmed Abdelali, Nadir Durrani, Fahim Dalvi +1
Arabic is a Semitic language which is widely spoken with many dialects. Given the success of pre-trained language models, many transformer models trained on Arabic and its dialects…