This paper explores a simpler method, called sampling and voting, to improve the performance of large language models (LLMs) by scaling up the number of agents used. The method involves generating multiple outputs from LLMs and using majority voting to decide the final response. Thorough experiments demonstrate its consistency and significant performance improvements, simplifying complex LLM applications.
Scaling Up LLM Agents: Unlocking Enhanced Performance Through Simplicity
Large language models (LLMs) are powerful but can struggle with precise reasoning. Recent solutions add complexity, but what if a simpler strategy could lead to significant gains?
The Sampling-and-Voting Method
This method involves two phases:
- Sampling: The task query is repeatedly fed into an LLM, generating multiple outputs (samples).
- Voting: Majority voting determines the final answer. For open-ended tasks, similarity measures are used to rank samples.
The method’s efficacy is extensively evaluated across three tasks and various language models, showing that increasing the number of agents generally boosts LLM performance across tasks and models of varying sizes.
Thorough experiments demonstrate the method’s consistency across hyperparameters and reveal that performance gains positively correlate with task difficulty. These findings inspired optimizations like stepwise or hierarchical sampling-and-voting, maximizing gains through a nuanced understanding of task difficulty.
This work establishes a new benchmark, demonstrating that scaling up LLM agents with a simple sampling-and-voting strategy significantly improves performance without intricate methods. This discovery simplifies complex LLM applications and paves the way for cost-optimization of future systems, a focus of ongoing research.
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