Son The Nguyen- Improve LLMs Alignment with Complete and Robust Preference Data | PyData Global 2024
www.pydata.org
This talk explores how to align large language models (LLMs) with human values via preference learning (PL) in the presence of challenges such as incomplete and corrupted data in preference datasets. We propose a novel method for recalibrating values to tackle these issues, enhancing LLM resilience by improving the robustness of existing models. The session highlights real-world experiments that show how the method addresses adversarial noise and unobserved comparisons, making it essential for building more reliable, ethically aligned AI systems.
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