Sample & Voting Strategy | Reducing LLM Costs & Improving Reliability
AI Summary
Summary: Enhancing AI with Multiple Agents
- Introduction
- AI models struggle with complex tasks.
- A method to improve AI effectiveness by up to 24% using multiple agents.
- Methodology
- The paper “More Agents is All You Need” introduces a method using multiple agents.
- Sampling and voting technique: multiple instances of an LLM answer the same question and vote on the best response.
- Effectiveness increases with task difficulty, peaking at 40 agents but beneficial with even 10 agents.
- Cost and Performance
- The method is cost-effective, enhancing cheaper models to outperform more expensive ones.
- GPT-3.5 is cheaper than GPT-4, allowing for more queries within the same budget.
- Smaller models are faster, and concurrent sampling speeds up the process.
- Voting Process
- Produce N samples.
- Calculate a cumulative similarity score for each sample.
- Select the sample with the highest score.
- Applications and Benefits
- Useful in healthcare, finance, and legal services for more reliable AI outputs.
- Reduces errors, hallucinations, and biases.
- Organizations can use multiple small models instead of one large model.
- Compatibility with Other Methods
- Can be combined with other techniques like RAG, Chain of Thought prompting, and multi-agent collaboration for further improvements.
- Limitations
- Diminishing returns after about 40 samples.
- More samples can be experimented with for complex tasks.
- Future Implications
- Ensemble methods offer new ways to integrate AI into organizations.
- Potential for new fields like AI-human interaction.
- Human oversight can refine the AI-generated solutions.
- Conclusion
- The paper encourages experimentation with AI systems to enhance performance.