The MAPGD framework addresses the limitations of existing prompt engineering methods that often rely on a singular optimization trajectory. By employing specialized agents for diverse roles such as task clarity and example selection, MAPGD enhances the prompt design process. The integration of semantic gradient coordination also serves to mitigate conflicts, optimizing the overall efficiency of prompt engineering.
One of the key innovations of MAPGD is its bandit-based candidate selection, which adeptly navigates the challenges of exploration and exploitation in prompt optimization. The results of various experiments indicate that MAPGD significantly surpasses traditional single-agent and random methodologies in both accuracy and effectiveness. This framework not only displays promising performance but also offers theoretical convergence guarantees, suggesting robust applicability across different tasks.
👉 Pročitaj original: arXiv AI Papers
