Semantic backpropagation lets AI agents learn from natural language feedback
This paper introduces semantic backpropagation and semantic gradients to optimize LLM-based agentic systems. The method generalizes mathematical gradients to handle natural language, enabling automatic optimization of graph-based agent systems while properly incorporating neighborhood information during backpropagation.
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https://arxiv.org/abs/2412.03624
🤖 Original Problem:
→ Current methods for optimizing LLM-based agentic systems require substantial manual effort. While representing these systems as computational graphs enables automatic optimization, existing approaches like TextGrad and OptoPrime fail to properly assign feedback across system components.
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🔧 Solution in this Paper:
→ The paper formalizes semantic gradients as a generalization of mathematical gradients for natural language optimization.
→ Semantic backpropagation propagates directional information through the graph while considering relationships between nodes with common successors.
→ Semantic gradient descent uses these gradients to update optimizable parameters like prompts and instructions.
→ The method includes an update gating mechanism that only accepts parameter updates if they improve performance on a validation set.
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💡 Key Insights:
→ Incorporating neighborhood node information during backpropagation is crucial for effective optimization
→ Update gating helps prevent the solution from deviating into less favorable regions
→ The method is parsimonious - removing key components leads to performance degradation
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📊 Results:
→ Outperforms TextGrad and OptoPrime on BIG-Bench Hard (82.5% NLP, 85.6% Algorithmic vs 48.7%, 66.9%)
→ Achieves 93.2% accuracy on GSM8K vs 78.2% for TextGrad
→ Maintains strong performance even with weaker LLMs (78.77% on GSM8K with Llama)
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