5 papers
Mitigating Extrinsic Gender Bias for Bangla Classification Tasks
Sajib Kumar Saha Joy, Arman Hassan Mahy, Meherin Sultana +4
In this study, we investigate extrinsic gender bias in Bangla pretrained language models, a largely underexplored area in low-resource languages. To assess this bias, we construct…
SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning
Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1
The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…
LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
Md Kowsher, Md. Shohanur Islam Sobuj, Nusrat Jahan Prottasha +3
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves fo…
OptiSeq: Ordering Examples On-The-Fly for In-Context Learning
Rahul Atul Bhope, Praveen Venkateswaran, K. R. Jayaram +3
Developers using LLMs and LLM-based agents in their applications have provided plenty of anecdotal evidence that in-context-learning (ICL) is fragile. In this paper, we show that i…
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
Mikayel Samvelyan, Sharath Chandra Raparthy, Andrei Lupu +9
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to adversarial attacks is of paramou…