4 papers · 1 filter
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…
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…
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…
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…