26/04/2024
💥 LLM Selection: Expert Guidance and Best Practices
Let's reflect on an insightful session by Yuval Belfer from AI21 Labs. Our speaker touched on the topic of LLM selection in a recent speech at the Epic AI Dev Summit.
Choosing the right Language Model can be the key to unlocking transformative results. From accelerating development timelines to improving text data processing.
Here are the key takeaways:
📝 Optimize Prompting
– Ask the Right Questions
Various models may respond differently to the same prompt due to varying tokenizers and training methods. By analyzing accuracy results, particularly focusing on the ending of prompts, you can uncover significant differences in performance.
– Use HELM – https://lnkd.in/gnutKyjj
HELM provides a structured methodology for evaluating LLMs, ensuring thorough testing and informed decision-making. It offers a comprehensive framework for evaluating foundation models, ensuring your questions align with your goals.
– Represent Your Actual Task
Utilize inputs that accurately represent your project's tasks and objectives. This provides insights into how the models will perform in real-world scenarios, helping you make informed decisions aligned with your actual use case.
🤖 Don’t rely solely on automatic metrics
– Evaluate Based on Models – https://chat.lmsys.org/
Run JudgeLM like Chatbot Arena to mimic human evaluation. Utilizing prompts and completions with an unbiased LM provides valuable insights into response quality.
– Assess Impartially
JudgeLM offers valuable feedback, but be mindful of its limitations, including inherent bias towards itself. Opt for impartial models for accurate evaluations.
– Conduct Human Checks
Manual checks are still important to ensure a model meets your standards beyond mere numbers, even when your resources are tight.
🤔 Make Informed Decisions
Models with contextual responses, prioritizing "helpfulness" as a deciding factor might seem tempting. However, for businesses, preventing hallucinations holds far greater significance. A low score in this parameter suggests that the model invents information without context.
Ensure your decision aligns with your project's objectives and requirements for optimal outcomes.
💻 Share your experiences! What challenges have you faced in selecting Language Models for your projects, and how did you overcome them?