Director Data Science at Sigmoid| Ex-VP Machine Learning at JPMorgan Chase & Co. | Patent Inventor | MDI, Gurgaon
𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 Unlike traditional software, which often focuses on functional testing for specific predetermined outcomes, evaluating LLM applications involves a broader and more nuanced spectrum of considerations 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐧𝐠 𝐋𝐋𝐌𝐬 𝐢𝐬 𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭: 𝐁𝐥𝐚𝐜𝐤 𝐁𝐨𝐱: Unlike traditional models, LLMs are complex models. We can't pinpoint why they generate a specific output, making debugging trickier. 𝐍𝐮𝐚𝐧𝐜𝐞𝐬 𝐨𝐟 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞: Unlike a program spitting out a single answer, LLMs can be creative! This means evaluating factors like factual accuracy, coherence, and even user preference become important 𝐌𝐨𝐯𝐢𝐧𝐠 𝐓𝐚𝐫𝐠𝐞𝐭𝐬: LLMs are constantly learning and evolving. An evaluation benchmark today might not be relevant tomorrow. LLMs offer incredible potential, but effective evaluation is key to unlocking their true value. #AI #MachineLearning #LLMs #Evaluation
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
8moEvaluating large language models (LLMs) indeed presents unique challenges due to their inherent complexity and dynamic nature. You talked about evaluating LLMs in your post. Considering the intricacies involved, one might wonder: How do you propose addressing the challenge of interpretability in LLM outputs, especially when dealing with sensitive or high-stakes applications such as medical diagnosis or legal document analysis? If, for instance, we imagine a scenario where LLMs are utilized for real-time decision-making in critical healthcare settings, how would you technically ensure the reliability and accountability of their outputs in such high-pressure environments?