88 citations · 140 across the 15 of their papers we have counts for
16 papers
Exploring Robustness of Multilingual LLMs on Real-World Noisy Data
Amirhossein Aliakbarzadeh, Lucie Flek, Akbar Karimi
Large Language Models (LLMs) are trained on Web data that might contain spelling errors made by humans. But do they become robust to similar real-world noise? In this paper, we inv…
Enforcing Fundamental Relations via Adversarial Attacks on Input Parameter Correlations
Timo Saala, Lucie Flek, Alexander Jung +5
Correlations between input parameters play a crucial role in many scientific classification tasks, since these are often related to fundamental laws of nature. For example, in high…
Exploring Robustness of LLMs to Paraphrasing Based on Sociodemographic Factors
Pulkit Arora, Akbar Karimi, Lucie Flek
Despite their linguistic prowess, LLMs have been shown to be vulnerable to small input perturbations. While robustness to local adversarial changes has been studied, robustness to…
ArithmAttack: Evaluating Robustness of LLMs to Noisy Context in Math Problem Solving
Zain Ul Abedin, Shahzeb Qamar, Lucie Flek +1
While Large Language Models (LLMs) have shown impressive capabilities in math problem-solving tasks, their robustness to noisy inputs is not well-studied. We propose ArithmAttack t…
Do Multilingual Large Language Models Mitigate Stereotype Bias?
Shangrui Nie, Michael Fromm, Charles Welch +7
While preliminary findings indicate that multilingual LLMs exhibit reduced bias compared to monolingual ones, a comprehensive understanding of the effect of multilingual training o…
Self-Balanced R-CNN for Instance Segmentation
Leonardo Rossi, Akbar Karimi, Andrea Prati
Current state-of-the-art two-stage models on instance segmentation task suffer from several types of imbalances. In this paper, we address the Intersection over the Union (IoU) dis…
CAISA at SemEval-2023 Task 8: Counterfactual Data Augmentation for Mitigating Class Imbalance in Causal Claim Identification
Akbar Karimi, Lucie Flek
The class imbalance problem can cause machine learning models to produce an undesirable performance on the minority class as well as the whole dataset. Using data augmentation tech…
Improving Localization for Semi-Supervised Object Detection
Leonardo Rossi, Akbar Karimi, Andrea Prati
Nowadays, Semi-Supervised Object Detection (SSOD) is a hot topic, since, while it is rather easy to collect images for creating a new dataset, labeling them is still an expensive a…
AEDA: An Easier Data Augmentation Technique for Text Classification
Akbar Karimi, Leonardo Rossi, Andrea Prati
This paper proposes AEDA (An Easier Data Augmentation) technique to help improve the performance on text classification tasks. AEDA includes only random insertion of punctuation ma…
UniParma at SemEval-2021 Task 5: Toxic Spans Detection Using CharacterBERT and Bag-of-Words Model
Akbar Karimi, Leonardo Rossi, Andrea Prati
With the ever-increasing availability of digital information, toxic content is also on the rise. Therefore, the detection of this type of language is of paramount importance. We ta…
Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing
Leonardo Rossi, Akbar Karimi, Andrea Prati
Within the field of instance segmentation, most of the state-of-the-art deep learning networks rely nowadays on cascade architectures, where multiple object detectors are trained s…
Improving BERT Performance for Aspect-Based Sentiment Analysis
Akbar Karimi, Leonardo Rossi, Andrea Prati
Aspect-Based Sentiment Analysis (ABSA) studies the consumer opinion on the market products. It involves examining the type of sentiments as well as sentiment targets expressed in p…
A novel Region of Interest Extraction Layer for Instance Segmentation
Leonardo Rossi, Akbar Karimi, Andrea Prati
Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular atte…
Deep Multimodal Image-Text Embeddings for Automatic Cross-Media Retrieval
Hadi Abdi Khojasteh, Ebrahim Ansari, Parvin Razzaghi +1
This paper considers the task of matching images and sentences by learning a visual-textual embedding space for cross-modal retrieval. Finding such a space is a challenging task si…
Adversarial Training for Aspect-Based Sentiment Analysis with BERT
Akbar Karimi, Leonardo Rossi, Andrea Prati
Aspect-Based Sentiment Analysis (ABSA) deals with the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize…
Extracting an English-Persian Parallel Corpus from Comparable Corpora
Akbar Karimi, Ebrahim Ansari, Bahram Sadeghi Bigham
Parallel data are an important part of a reliable Statistical Machine Translation (SMT) system. The more of these data are available, the better the quality of the SMT system. Howe…