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20162026
most citedWhat do Neural Machine Translation Models Learn about Morphology?

281 citations · 652 across the 50 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CL2023

Scaling up Discovery of Latent Concepts in Deep NLP Models

Majd Hawasly, Fahim Dalvi, Nadir Durrani

Despite the revolution caused by deep NLP models, they remain black boxes, necessitating research to understand their decision-making processes. A recent work by Dalvi et al. (2022…

cs.CL2023★ 4 cited

LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking

Fahim Dalvi, Maram Hasanain, Sabri Boughorbel +10

The recent development and success of Large Language Models (LLMs) necessitate an evaluation of their performance across diverse NLP tasks in different languages. Although several…

cs.CL2023

NeuroX Library for Neuron Analysis of Deep NLP Models

Fahim Dalvi, Hassan Sajjad, Nadir Durrani

Neuron analysis provides insights into how knowledge is structured in representations and discovers the role of neurons in the network. In addition to developing an understanding o…

cs.CL2023★ 14 cited

LAraBench: Benchmarking Arabic AI with Large Language Models

Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury +13

Recent advancements in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. Despite this progress, these models lack specific b…

cs.CL2023

Can LLMs facilitate interpretation of pre-trained language models?

Basel Mousi, Nadir Durrani, Fahim Dalvi

Work done to uncover the knowledge encoded within pre-trained language models rely on annotated corpora or human-in-the-loop methods. However, these approaches are limited in terms…

cs.CL2023

NxPlain: Web-based Tool for Discovery of Latent Concepts

Fahim Dalvi, Nadir Durrani, Hassan Sajjad +3

The proliferation of deep neural networks in various domains has seen an increased need for the interpretability of these models, especially in scenarios where fairness and trust a…

cs.CL2023★ 2 cited

Evaluating Neuron Interpretation Methods of NLP Models

Yimin Fan, Fahim Dalvi, Nadir Durrani +1

Neuron Interpretation has gained traction in the field of interpretability, and have provided fine-grained insights into what a model learns and how language knowledge is distribut…