Large language models (LLMs) have revolutionized natural language processing, enabling tasks such as text generation and language translation with impressive accuracy. However, these models can be computationally expensive and require significant resources to train and run. In this article, the authors propose a novel approach called interactive critiquing to improve the efficiency of LLMs without sacrificing their performance.
Interactive Critiquing
The authors introduce the concept of interactive critiquing, which involves feeding the output of an LLM back into the model as input to generate a revised version. This process allows the model to learn from its mistakes and improve its performance over time. The authors argue that interactive critiquing can significantly reduce the number of tokens used by LLMs while maintaining their accuracy.
Explore, Select, and Derive
The authors propose a three-step process called explore, select, and derive to identify actionable sub-tasks for a given screen. This process involves exploring the screen to generate a list of potential sub-tasks, selecting the most relevant tasks, and then deriving the primitive actions required to complete each task. The authors claim that this approach can significantly reduce the number of tokens used by LLMs while maintaining their accuracy.
Screen Representation
The authors introduce the concept of screen representation, which involves representing an app screen as a list of actionable sub-tasks. This approach allows for easy recall and repetition of not only the task itself but also its involved sub-tasks. The authors argue that screen representation can significantly improve the efficiency of LLMs by reducing their token count.
Results
The authors present several results to demonstrate the effectiveness of interactive critiquing. They show that their approach can reduce the number of tokens used by LLMs by an average of 85.2% while maintaining their accuracy. The authors also demonstrate the efficacy of screen representation by showing that it can significantly improve the efficiency of LLMs.
Conclusion
In conclusion, the authors propose interactive critiquing as a novel approach to improving the efficiency of large language models without sacrificing their performance. They introduce the concepts of explore, select, and derive for identifying actionable sub-tasks and screen representation for representing app screens. The authors show that these approaches can significantly reduce the number of tokens used by LLMs while maintaining their accuracy. Overall, the article provides a valuable contribution to the field of natural language processing and highlights the potential of interactive critiquing for improving the efficiency of LLMs in the future.
