PIE (Pseudocode-Injection-Enhanced) framework enhances LLM graph reasoning by delegating structure processing and injecting pseudocode.
This paper enhances LLMs' ability to solve graph computational tasks by delegating graph structure understanding to an interpreter and focusing LLMs on code generation guided by pseudocode.
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Paper - https://arxiv.org/abs/2501.13731
Original Problem : 😔:
→ LLMs struggle with graph computational tasks due to limited graph structure comprehension and high inference costs.
→ Existing methods serialize graph structures into text, hindering LLM understanding and increasing computational burden.
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Solution in this Paper : 💡:
→ PIE framework (Pseudocode-Injection-Enhanced LLM Reasoning) separates task understanding and graph structure interpretation.
→ LLMs focus on understanding the task and generating code, while an interpreter handles graph structure and executes the generated code.
→ Pseudocode injection guides LLMs to generate efficient, task-specific code.
→ Trial-and-error with small-scale graphs refines code correctness before applying it to larger graphs.
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Key Insights from this Paper : 🤔:
→ Delegating graph structure processing to specialized interpreters significantly improves accuracy and efficiency.
→ Pseudocode injection leverages LLMs' code generation capabilities and avoids brute-force approaches.
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Results : 🚀:
→ Achieves 100% accuracy on polynomial-time tasks.
→ Significantly outperforms baselines on NP-complete tasks with an average improvement of over 58 percentage points on large graphs using Llama3-8b/70b.
→ Reduces LLM inference cost by requiring significantly fewer calls.
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