7 papers
Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages
Tarek Naous, Anagha Savit, Carlos Rafael Catalan +17
As Large Language Models (LLMs) develop stronger multilingual capabilities, their sensitivity to culturally diverse entities becomes increasingly important. Prior work by Naous et…
Belief-Sim: Towards Belief-Driven Simulation of Demographic Misinformation Susceptibility
Angana Borah, Zohaib Khan, Rada Mihalcea +1
Misinformation is a growing societal threat, and susceptibility to misinformative claims varies across demographic groups due to differences in underlying beliefs. As Large Languag…
Countdown-Code: A Testbed for Studying The Emergence and Generalization of Reward Hacking in RLVR
Muhammad Khalifa, Zohaib Khan, Omer Tafveez +2
Reward hacking is a form of misalignment in which models overoptimize proxy rewards without genuinely solving the underlying task. Precisely measuring reward hacking occurrence rem…
To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs
Zohaib Khan, Mustafa Dogan, Ifeoma Okoh +6
Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information. We study how LLMs be…
Plasticity vs. Rigidity: The Impact of Low-Rank Adapters on Reasoning on a Micro-Budget
Zohaib Khan, Omer Tafveez, Zoha Hayat Bhatti
Recent advances in mathematical reasoning typically rely on massive scale, yet the question remains: can strong reasoning capabilities be induced in small language models ($\leq1.5…
Scaling Truth: The Confidence Paradox in AI Fact-Checking
Ihsan A. Qazi, Zohaib Khan, Abdullah Ghani +7
The rise of misinformation underscores the need for scalable and reliable fact-checking solutions. Large language models (LLMs) hold promise in automating fact verification, yet th…