Papers by Mohammad Pilehvar
DiFair: A Benchmark for Disentangled Assessment of Gender Knowledge and Bias (2023.findings-emnlp)
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| Challenge: | Existing methods to mitigate gender bias in pre-trained language models are often evaluated on datasets that check the extent to which the model is gender-neutral in its predictions. |
| Approach: | They propose to use a manually curated dataset to measure gender bias and to measure useful gender knowledge. |
| Outcome: | The proposed dataset aims to quantify gender biases and to assess their impact on useful gender knowledge. |
RepMatch: Quantifying Cross-Instance Similarities in Representation Space (2024.emnlp-main)
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| Challenge: | Recent advances in dataset analysis have enabled more sophisticated approaches to analyzing and characterizing training data instances. |
| Approach: | They propose a method that characterizes data through the lens of similarity. |
| Outcome: | The proposed method can compare datasets, identify more representative subsets, and uncover heuristics underlying the construction of some challenge datasets. |
DadmaTools: Natural Language Processing Toolkit for Persian Language (2022.naacl-demo)
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| Challenge: | Existing tools for Persian language processing are based on conventional non-neural models and do not take full advantage of the latest developments. |
| Approach: | They propose to use a Python neural pipeline for Persian text processing tasks . they use 'parsBERT' to fine-tune the Python pipeline using the PerDT dataset . |
| Outcome: | The proposed toolkit can achieve state-of-the-art performance on multiple NLP tasks. |
Stochastic Fine-Tuning of Language Models Using Masked Gradients (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are the dominant paradigm in Natural Language Processing but fine-tuning them for specific downstream tasks often requires updating a vast number of parameters. |
| Approach: | They propose a method that selectively updates a small subset of parameters in each step of the tuning process. |
| Outcome: | The proposed approach outperforms existing fine-tuning methods while updating merely **0.08**% of the model’s parameters. |
An Empirical Study on the Transferability of Transformer Modules in Parameter-efficient Fine-tuning (2022.emnlp-main)
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| Challenge: | Parameter-efficient fine-tuning is a computationally expensive process . introducing new parameters to an already-large model can be considered a drawback. |
| Approach: | They investigate the capability of different transformer modules in transferring knowledge from a pre-trained model to a downstream task. |
| Outcome: | The proposed methods show that each transformer module is a winning ticket . they show that with only 0.003% updateable parameters, they can show acceptable performance on target tasks. |