Patched MOA boosts the performance of smaller models to surpass that of larger ones.
It boosts gpt-4o-mini's performance by 15.52% on Arena-Hard-Auto, outperforming gpt-4-turbo.
📚 https://arxiv.org/abs/2407.18521
Original Problem 🔍:
LLM inference for complex multi-step reasoning workflows needs to be fast, cheap, and accurate. Optimizing smaller models to match larger ones' performance.
-----
Solution in this Paper 🛠️:
• Introduces Patched MOA (Mixture of Agents) for LLM inference optimization
• Evaluates three techniques: Best of N, Mixture of Agents, Monte Carlo Tree Search
• Applies optimization to gpt-4o-mini model
• Uses Arena-Hard-Auto benchmark for performance evaluation
• Implements technique in open-source optillm framework
-----
Key Insights from this Paper 💡:
• Patched MOA boosts gpt-4o-mini performance by 15.52% on Arena-Hard-Auto benchmark
• Outperforms gpt-4-turbo at 1/50th the cost
• Model-agnostic approach, transparent to end-users
• Applicable to various software development workflows
• Consistent improvements in task completion rates across different patchflows
-----
Results 📊:
• moa-gpt-4o-mini: 85.6 score (Arena-Hard-Auto benchmark)
• Outperforms gpt-4-turbo-2024-04-09 (82.6 score)
• Improves performance across all tested patchflows:
- AutoFix: 41.18% to 46.67%
- PRReview: 50% to 100%
- GenerateDocstring: 71.21% to 89.52%
- GenerateREADME: 66.67% to 71.43%
- ResolveIssue: 61.11% to 85.71%
