How to Correctly do Semantic Backpropagation on Language-based Agentic Systems (2412.03624v1)

Abstract: Language-based agentic systems have shown great promise in recent years, transitioning from solving small-scale research problems to being deployed in challenging real-world tasks. However, optimizing these systems often requires substantial manual labor. Recent studies have demonstrated that these systems can be represented as computational graphs, enabling automatic optimization. Despite these advancements, most current efforts in Graph-based Agentic System Optimization (GASO) fail to properly assign feedback to the system's components given feedback on the system's output. To address this challenge, we formalize the concept of semantic backpropagation with semantic gradients -- a generalization that aligns several key optimization techniques, including reverse-mode automatic differentiation and the more recent TextGrad by exploiting the relationship among nodes with a common successor. This serves as a method for computing directional information about how changes to each component of an agentic system might improve the system's output. To use these gradients, we propose a method called semantic gradient descent which enables us to solve GASO effectively. Our results on both BIG-Bench Hard and GSM8K show that our approach outperforms existing state-of-the-art methods for solving GASO problems. A detailed ablation study on the LIAR dataset demonstrates the parsimonious nature of our method. A full copy of our implementation is publicly available at https://github.com/HishamAlyahya/semantic_backprop

• The paper introduces semantic backpropagation to compute semantic gradients, extending reverse-mode automatic differentiation for agentic systems optimization.
• Empirical results show that semantic gradient descent significantly outperforms existing methods on benchmarks like GSM8K and BBH.
• Ablation studies confirm that integrating neighborhood feedback enhances system performance, validating the method's robustness.

An Evaluation of Semantic Backpropagation for Agentic Systems Optimization

The paper under review presents a comprehensive approach to optimizing language-based agentic systems through the proposal of a novel method termed Semantic Backpropagation, coupled with Semantic Gradient Descent. As the authors outline, the Graph-based Agentic System Optimization (GASO) problem serves as the crux in designing systems that are both flexible and efficient, requiring the optimization of parameters in large computational graphs employed by agentic systems. These graphs utilize LLMs as core components, where each node in the graph represents functions that are customizable based on said models.

The primary contribution of this paper is the introduction and formalization of Semantic Backpropagation, which extends the concept of reverse-mode automatic differentiation to compute semantic gradients over the graph of an agentic system. This involves using feedback about the system's performance to determine directional information—denoted as semantic gradients, providing insight into how changes to specific nodes can lead to improved outcomes. The authors delineate this methodology from existing GASO solutions such as TextGrad and OptoPrime, arguing it rectifies the limitations seen in these methods by integrating neighborhood information, which enhances the accuracy of the gradient's feedback signal.

Empirical evaluations of the proposed method demonstrate superior performance across a range of benchmarks, including the BIG-Bench Hard (BBH), GSM8K, and BigCodeBench (BCB) datasets. Semantic Gradient Descent outperforms TextGrad and OptoPrime in GSM8K, and it achieves higher scores than COPRO in BBH. The paper underscores these outcomes with detailed experiments, examining both task-wide and subtask-wise results, confirming its efficacy notably in scenarios characterized by complex prompt structures.

Key claims made include that Semantic Gradient Descent addresses the drawback of existing methods by employing a refined approach to gradient accumulation that is sensitive to neighboring node interactions. The authors justify their claim with quantitative evidence, where the proposed method showed a significantly higher performance improvement over baselines across benchmark datasets.

Furthermore, an ablation paper on the LIAR dataset reinforces this method's robustness, suggesting that components like semantic gradients and neighborhood consideration are integral to the system's performance. The paper highlights that removing these components causes observable deterioration in system output quality, thereby validating the paper's theoretical foundations and the practical need for both enhanced neighborhood integration in backpropagation algorithms and maintaining a semantic orientation in optimization pursuits.

The implications of this research are substantial, with potential applications extending to various complex agentic systems that rely on LLMs for decision-making and problem-solving tasks. The introduction of semantic gradients and the novel optimization framework signal a critical shift toward more intelligent, adaptive systems capable of refining their components based on nuanced feedback mechanisms. Future work could delve into expanding this framework to accommodate larger, more intricate computational graph structures and exploring the impacts of diverse model types in the semantic backpropagation process.

In summary, the paper presents a transparent and methodologically sound approach to tackling the inherent challenges in optimizing intricate LLM-based agent systems. Its ability to leverage semantic feedback to perform precise and context-aware system refinements denotes a step forward in AI system optimization, presenting intriguing avenues for further paper in agentic systems and beyond.