11-04, 11:30–12:00 (Asia/Jerusalem), Red Track
This lecture on Tackling Prompt Injection focuses on addressing the challenges posed by biased, misleading, or unethical prompts in language models, and the utilization of the LangChain framework to tackle this effort. Prompt injection has emerged as a critical concern affecting the reliability, fairness, and ethical use of language models. In this lecture, we explore innovative methodologies, techniques, and strategies to detect, mitigate, and prevent prompt injection. We delve into the quantitative and qualitative evaluation of prompt injection vulnerabilities, the resilience of language models against adversarial attacks, and the ethical considerations in prompt design and usage. Moreover, we showcase the LangChain framework as a powerful tool to secure language models against prompt injection, ensuring trustworthiness and integrity in data-driven decision-making. Join us for this enlightening lecture and discover how to combat prompt injection and leverage LangChain to enhance data science applications.
This lecture on Tackling Prompt Injection is designed for researchers, data scientists, and professionals in the field of data science who are interested in understanding and mitigating prompt injection challenges in language models. The lecture will delve into the issues related to prompt injection, such as biased or misleading prompts that can lead to undesired outputs or unethical decision-making. Attendees will gain insights into state-of-the-art methodologies and techniques for detecting and mitigating prompt injection vulnerabilities.
The lecture will cover various topics, including the evaluation of prompt injection vulnerabilities using both quantitative and qualitative approaches, the resilience of language models against adversarial attacks, and the ethical considerations in prompt design and usage.
Through practical examples, case studies, and demonstrations, participants will acquire a deeper understanding of prompt injection challenges and explore effective strategies to prevent and address them. By leveraging the LangChain framework, attendees will learn how to enhance the trustworthiness and integrity of language models in data science applications.
Join me for this informative lecture and discover valuable insights, methodologies, and approaches to tackle prompt injection, paving the way for more reliable, fair, and ethical data-driven decision-making.
Hello! I'm Michael, a Data Scientist with over 4 years of experience, specializing in developing advanced algorithms for fraud prevention in the fintech industry.
Currently, I work as a Data Scientist within the risk department at Melio, a rapidly growing fintech company.
Additionally, I'm a mentor at Masterschool, where I work closely with my mentees to help them achieve their goals, stay motivated and on track.
Alongside my work in data science, I'm also an avid ultra-marathon runner and a former coach. I believe that maintaining a healthy mind and body is essential for a fulfilling life and enjoy pushing myself to new physical and mental limits.
I'm always looking for opportunities to collaborate and make a positive impact in the world. If you're interested in connecting with me or learning more about my work, feel free to send me a message!