We're Dealing with the Butterfly Effect in LLM Creation Pipelines 🦋🤖
When we develop novel LLMs, AI-Agents and GenAI Pipelines for Alan.de or within our various other GenAI projects, we're continuously learning about the Butterfly Effect in GenAI creation pipelines and how to mitigate this problem. 👨🏼💻 🤯 Every change can have a huge impact on the entire pipeline that creates and executes models
TL;DR ⏱️
- The Butterfly Effect can significantly impact LLM creation pipelines.
- Small changes in preprocessing or generation configs can have large ripple effects.
- Constantly evaluating trade-offs and staying up-to-date with new models is crucial.
- Strategies to mitigate the Butterfly Effect are essential for optimal performance.
The Butterfly Effect in action:
- 📃 Example 1: Preprocessing differences in raw-document data extraction can result in different extracted text, facts, training and benchmarking data, and ultimately, different model performance and benchmark results.
- 📊 Example 2: Even a slight change in the generation config (e.g. top_p) of a Benchmarking-Data generating AI Agent can have a ripple effect on the entire pipeline to the finally selected best LLM.
The Challenge:
- 👩🏻🔬 With new models and agents (e.g. LLM-as-Judge) becoming available every day, we need to be prepared to exchange components frequently to stay up-to-date and ensure optimal performance and reliability of our pipeline
- ⚖️ This means constantly evaluating the trade-offs between performance, reliability, and interpretability, and making informed decisions about when to update or replace components.
My Takes:
- 🧐 Be aware of the Butterfly Effect in your LLM creation pipelines and take steps to mitigate its impact.
- 👨🏼🎓 Stay up-to-date with the latest developments (insane task) in the field and be prepared to adapt and evolve your solutions accordingly.
My Questions:
- 🦋 How do you handle the Butterfly Effect in your LLM creation pipelines?
- 🗞️ What strategies do you use to stay up-to-date with the latest developments in the field and ensure optimal performance?
In recent and especially upcoming posts I will address how we Comma Soft AG handle this challenge. If you like to see more of the details please leave me a comment what you think and which questions and ideas you have 💬❤️
#generativeai #artificialintelligence #llm #machinelearning #alan