Optimizing Large Language Models with Reinforcement Learning-Based Prompts
15see more slides, notes, and other material here: https://github.com/Aggregate-Intellect/practical-llms/
https://www.linkedin.com/in/mingkaideng/
Large language models (LLMs) are versatile and can perform tasks like summarization, code generation, sentiment analysis, dialogue, translation, and storytelling depending on the prompt.
The wording of the prompt can significantly affect LLMs’ performance, making it challenging to find the best prompt for a given task. Two prompts with the same meaning can lead to different outcomes.
Prompt optimization is a challenging problem due to the large number of candidates. One way to address it is to formulate it as a reinforcement learning problem. This allows for more effective identification of the best prompts.
The reinforcement learning approach involves training a prompt policy to learn correlations between words and their underlying score or reward. It is a powerful way to optimize prompts for large language models.
Optimized prompts for RL problems can perform better than human-written prompts, even if they don’t follow human language. This utility of RL prompts is important to understand.
The optimized prompts can transfer well across models, and the reinforcement learning-based optimization allows for more effective identification of the best prompts. Careful optimization is key for large language models.
I developed a framework that combines a smaller language model for word correlations and a larger model for tasks. It can perform few-shot text classification and unsupervised control text generation. #MachineLearning #NLP
Optimized prompts for the framework are consistently among the best performers, unlike manual prompts, which can vary widely in performance. Check out my graph comparing their performance across different models. #AI #NLP
Shorter optimized prompts lead to faster model runs and lower costs. I found that optimized prompts trained on one model can also be applied to other models with similar or even better performance. #MachineLearning #Optimization
My framework is better than human-written prompts at capturing how language models respond to prompts. See the graph comparing the performance of manual prompts vs. optimized prompts. #NLP #DataScience
I made sure to package my framework code well and make it easy to set up. You can find it on GitHub. For instance, running a test style transfer experiment requires only 51 lines of code. #OpenSource #Python
Optimized prompts from my framework can even turn negative sentences into more positive ones while preserving the original meaning. Want to see a demo? #AI #NLP #SentimentAnalysis