More Generations Is All You Need🥂

🌈 Abstract

The article discusses a method of improving the performance of large language models (LLMs) by scaling up the number of agents (i.e., running the same query multiple times on the same LLM without any context shared across queries) and using a similarity algorithm to pick the most common answer. The article suggests that this simple approach can perform as well as or better than other multi-agent algorithms like Chain-of-thought and LLM-Debate, and raises questions about the effectiveness of these more complex multi-agent setups.

🙋 Q&A

[01] More Agents Is All You Need 🤖

1. What is the key idea proposed in the paper?

• The paper proposes a simple method of running the same query multiple times on the same LLM, without any context shared across queries, and then using a similarity algorithm to pick the most common answer.
• This approach is suggested to be as effective as or better than more complex multi-agent algorithms like Chain-of-thought and LLM-Debate.

2. How does this simple approach compare to other multi-agent setups?

• The paper suggests that the improved results from other multi-agent schemes are mostly due to the fact that the LLM is run multiple times and the prompt asks the LLM to pick the best answer.
• The paper argues that this simple "ensemble model" approach of averaging the outputs of multiple predictions has been used effectively in machine learning for a long time.

3. How does the performance scale with the number of agents?

• The paper shows that as the ensemble size (number of agents) scales up, the performance of LLMs like Llama2 and GPT-3.5-Turbo can achieve comparable accuracy to larger models like GPT-4.
• The performance can be further improved across different LLMs and tasks by scaling up the number of agents.