Aniket Kumar Singh, Vision Systems Engineer at Ultium Cells, discusses evaluating Large Language Models (LLMs), importance of prompt engineering, real-world applications in healthcare/economics/education, and future LLM improvements. Topics include performance metrics, model selection, task automation, personality impact on LLMs, agent architectures, fine-tuning processes, and challenges in evaluating LLM effectiveness.
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Quick takeaways
Evaluating LLMs based on practical knowledge and confidence levels, not just benchmarks.
Utilizing confidence scores to differentiate LLM models and assessing competence through feedback mechanisms.
Deep dives
Evaluating Language Model Performances in Different Scenarios
The podcast discusses Anakit's focus on evaluating LLMs not from a benchmarking standpoint but rather assessing their practical knowledge and confidence levels. Anakit delves into the importance of confidence scores and differentiates models based on their confidence levels, highlighting the need for practical applications of LLMs rather than just benchmarking in the evolving field.
Utilizing LLMs for Real-Life Scenarios like Auction Bidding
Anakit and Dimitri explore the concept of using LLMs to replace human tasks, such as bidding at auctions, to enhance efficiency and cost-effectiveness. They conducted experiments assigning personalities to LLMs to analyze their behavior, finding that certain models exhibited overconfidence, highlighting the nuances in modeling behavior and decision-making processes.
Stealth Assessment and Confidence Scores in LLMs
The podcast delves into stealth assessment methods where LLMs solve coding problems with varying confidence levels and feedback mechanisms. Anakit pioneered the use of confidence scores and feedback to assess model competence and alignment, citing instances of models adjusting their confidence based on feedback, showcasing the importance of gauging both absolute and relative confidence levels in LLM evaluation.
Aniket Kumar Singh is a Vision Systems Engineer at Ultium Cells, skilled in Machine Learning and Deep Learning. I'm also engaged in AI research, focusing on Large Language Models (LLMs).
Evaluating the Effectiveness of Large Language Models: Challenges and Insights // MLOps Podcast #248 with Aniket Kumar Singh, CTO @ MyEvaluationPal | ML Engineer @ Ultium Cells.
// Abstract
Dive into the world of Large Language Models (LLMs) like GPT-4. Why is it crucial to evaluate these models, how we measure their performance, and the common hurdles we face? Drawing from Aniket's research, he shares insights on the importance of prompt engineering and model selection. Aniket also discusses real-world applications in healthcare, economics, and education, and highlights future directions for improving LLMs.
// Bio
Aniket is a Vision Systems Engineer at Ultium Cells, skilled in Machine Learning and Deep Learning. I'm also engaged in AI research, focusing on Large Language Models (LLMs).
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// Related Links
Website: www.aniketsingh.me
Aniket's AI Research for Good blog that I plan to utilize to share any new research that would focus on the good: www.airesearchforgood.org
Aniket's papers: https://scholar.google.com/citations?user=XHxdWUMAAAAJ&hl=en
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Connect with Aniket on LinkedIn: https://www.linkedin.com/in/singh-k-aniket/
Timestamps:
[00:00] Aniket's preferred coffee
[00:14] Takeaways
[01:29] Aniket's job and hobby
[03:06] Evaluating LLMs: Systems-Level Perspective
[05:55] Rule-based system
[08:32] Evaluation Focus: Model Capabilities
[13:04] LLM Confidence
[13:56] Problems with LLM Ratings
[17:17] Understanding AI Confidence Trends
[18:28] Aniket's papers
[20:40] Testing AI Awareness
[24:36] Agent Architectures Overview
[27:05] Leveraging LLMs for tasks
[29:53] Closed systems in Decision-Making
[31:28] Navigating model Agnosticism
[33:47] Robust Pipeline vs Robust Prompt
[34:40] Wrap up
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