Multiple reasoning trees working together make LLMs think better than single-tree approaches.
Forest-of-Thought framework integrates multiple reasoning trees with sparse activation and self-correction to enhance LLM reasoning capabilities beyond single-pass approaches.
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https://arxiv.org/abs/2412.09078
🤔 Original Problem:
→ Current LLM reasoning methods like Chain-of-Thought and Tree-of-Thought perform only single-pass reasoning, leading to flawed paths and compromised accuracy.
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🌳 Solution in this Paper:
→ Forest-of-Thought (FoT) creates multiple independent reasoning trees to approach problems from different angles.
→ Sparse activation strategies select only the most relevant reasoning paths, optimizing both efficiency and accuracy.
→ Dynamic self-correction enables real-time error detection and correction during reasoning.
→ Consensus-guided expert decision making optimizes correctness and computational resource usage.
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🔍 Key Insights:
→ Multiple reasoning trees provide better collective decision-making than single-tree approaches
→ Sparse activation significantly improves computational efficiency
→ Self-correction mechanism prevents error propagation across reasoning steps
→ Consensus-guided decisions outperform random and score-based selection
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📊 Results:
→ FoT with 4 trees achieved 91.58% accuracy on Game of 24 tasks vs 74.74% for single-tree ToT
→ Dynamic self-correction improved accuracy by 5% for zero-shot-CoT and over 50% for ToT
→ CGED selection strategy outperformed random and score-based selection by 0.8-0.9% accuracy
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