Explore a 33-minute video lecture on enhancing Large Language Model (LLM) accuracy using Monte Carlo Tree Search. Dive into the process of boosting Llama 3 8B performance to rival GPT-4 on specific benchmarks. Understand the impact of prompting on accuracy and learn the mechanics of Monte Carlo tree search, including the balance between exploitation and exploration. Follow along with Jupyter Notebook code demonstrations, witness Monte Carlo Tree Search applied to a simple example, and discover its potential for improving performance on mathematical problems. Examine the limitations of Monte Carlo performance boosts and access additional resources for further study.
Improving LLM Accuracy with Monte Carlo Tree Search
Overview
Syllabus
Large Language Models Make Things Up!
Boosting Llama 3 8B performance to GPT-4 only on certain benchmarks!
How prompting affects accuracy
How Monte Carlo tree search works
Balancing exploitation with exploration
Jupyter Notebook Code
Testing Monte Carlo Tree Search on a simple example
Boosting Performance on Maths problems
Limitations on Monte Carlo Performance Boosts
Resources
Taught by
Trelis Research
