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cs.CL2024
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…
cs.AI2024
WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Seyedali Mohammadi, Edward Raff, Jinendra Malekar +3
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus te…
cs.CL2024
GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements
Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis +4
State-of-the-art language models can exhibit impressive reasoning refinement capabilities on math, science or coding tasks. However, recent work demonstrates that even the best mod…