6 papers
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias
Mahdi Zakizadeh, Kaveh Eskandari Miandoab, Mohammad Taher Pilehvar
Numerous debiasing techniques have been proposed to mitigate the gender bias that is prevalent in pretrained language models. These are often evaluated on datasets that check the e…
DecompX: Explaining Transformers Decisions by Propagating Token Decomposition
Ali Modarressi, Mohsen Fayyaz, Ehsan Aghazadeh +2
An emerging solution for explaining Transformer-based models is to use vector-based analysis on how the representations are formed. However, providing a faithful vector-based expla…
An Empirical Study on the Transferability of Transformer Modules in Parameter-Efficient Fine-Tuning
Mohammad Akbar-Tajari, Sara Rajaee, Mohammad Taher Pilehvar
Parameter-efficient fine-tuning approaches have recently garnered a lot of attention. Having considerably lower number of trainable weights, these methods can bring about scalabili…
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities
Ali Modarressi, Hossein Amirkhani, Mohammad Taher Pilehvar
Several proposals have been put forward in recent years for improving out-of-distribution (OOD) performance through mitigating dataset biases. A popular workaround is to train a ro…
De-Conflated Semantic Representations
Mohammad Taher Pilehvar, Nigel Collier
One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings th…
Semantic Representations of Word Senses and Concepts
José Camacho-Collados, Ignacio Iacobacci, Roberto Navigli +1
Representing the semantics of linguistic items in a machine-interpretable form has been a major goal of Natural Language Processing since its earliest days. Among the range of diff…