6 papers · 1 filter
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…
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…
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…
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…