Events Calendar
Welcome to your calendar for data science events! Dive into a curated list of courses, conferences, seminars, workshops, and key deadlines. Tailor your search to match your interests by adjusting the event category filters. For those specifically looking for PhD courses, simply modify the filter settings to include these events as well. Stay connected and up-to-date with the latest in data science, all in one place.
- This event has passed.
Robust Large Language Models: From Security to Factuality
Welcome to Robust Large Language Models: From Security to Factuality
Description:
The course will focus on two central topics related to robustness in LLMs: Security and Factuality
Security
The course will cover methods for detecting, preventing, and mitigating vulnerabilities in LLM-based systems. Although LLMs are trained with safety and helpfulness in mind, protecting them from adversarial manipulation and misuse remains a pressing challenge. Threats may range from straightforward prompt injections to more elaborate, multi-stage exploits targeting system instructions, fine-tuning data, or connected applications. Addressing security in LLMs requires multiple layers of defense, including careful safeguard design, thorough evaluation, and ongoing monitoring. Participants will learn core principles of LLM security, examine state-of-the-art defense and testing approaches, and work with structured evaluation protocols, including automated red-teaming for multi-turn dialogue scenarios. By the end, they will be able to design, assess, and strengthen LLM applications against real-world threats.
Factuality
The course will also focus on Uncertainty Quantification (UQ), a key approach for improving the reliability of LLM outputs. UQ is increasingly important in NLP for reducing hallucinations, identifying weak or erroneous responses, detecting out-of-distribution inputs, and optimising latency. While UQ is well studied in classification tasks, adapting it to LLMs is significantly more challenging due to the sequential and interdependent nature of generated text, where different tokens contribute unequally to meaning. Participants will gain an understanding of the main concepts in UQ for LLMs, survey current research and techniques, explore applications in diverse settings, and acquire practical skills for designing new UQ strategies to enhance factuality and trustworthiness in LLM-driven systems.
The course will further touch upon topics such as memorization, evaluation, hallucination.
For additional information, updates, and registration, please refer to AAU PhD Moodle via the link provided on the right side of this page.
Disclaimer:
DDSA has explicit permission from Arcanic and the owners of the https://phdcourses.dk/ website to display the courses on ddsa.dk.
