ADOPT: Optimizing LLM Pipelines with Adaptive Dependency Awareness
Analysis
This paper addresses the challenge of optimizing prompts in multi-step LLM pipelines, a crucial area for complex task solving. The key contribution is ADOPT, a framework that tackles the difficulties of joint prompt optimization by explicitly modeling inter-step dependencies and using a Shapley-based resource allocation mechanism. This approach aims to improve performance and stability compared to existing methods, which is significant for practical applications of LLMs.
Key Takeaways
Reference / Citation
View Original"ADOPT explicitly models the dependency between each LLM step and the final task outcome, enabling precise text-gradient estimation analogous to computing analytical derivatives."