Original Paper: https://arxiv.org/abs/2301.08721
By: Zhoujun Cheng, Jungo Kasai, Tao Yu
Abstract:
Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch prompting, a simple yet effective prompting approach that enables the LLM to run inference in batches, instead of one sample at a time. Our method reduces both token and time costs while retaining downstream performance. We theoretically demonstrate that under a few-shot in-context learning setting, the inference costs decrease almost inverse linearly with the number of samples in each batch. We extensively validate the effectiveness of batch prompting on ten datasets across commonsense QA, arithmetic reasoning, and NLI/NLU: batch prompting significantly~(up to 5x with six samples in batch) reduces the LLM (Codex) inference token and time costs while achieving better or comparable performance. For state-of-the-art Chat-based LLMs, e.g., GPT-3.5 and GPT-4, we show the benefits of batch prompting also hold. Further analysis shows that the number of samples in each batch and the complexity of tasks affect its performance. Moreover, batch prompting can be applied across different reasoning methods using LLMs. Our code can be found at the site
Summary Notes
Efficient Inference with Large Language Models: The Power of Batch Prompting
In the rapidly advancing field of artificial intelligence (AI) and machine learning, finding efficient and cost-effective methods is crucial, especially for AI engineers in the business world.
One innovative solution changing the game is batch prompting. This method is set to transform how we use large language models (LLMs) like OpenAI's GPT series.
Let's dive into what batch prompting is, its benefits, and its potential to revolutionize AI applications.
Traditional LLM Inference: The Challenges
Typically, LLMs handle inference tasks one sample at a time.
This approach is simple but leads to high computational and financial costs. With the growing demand for AI solutions, the need for more efficient methods has become clear, paving the way for batch prompting.
What is Batch Prompting?
Batch prompting is a novel approach that groups multiple samples into a single prompt, offering significant improvements:
- Reduced API Calls: Fewer API calls mean lower costs, crucial for businesses.
- Efficient Computational Load: Spreading the setup costs over multiple samples boosts efficiency.
The Science and Savings Behind Batch Prompting
Batch prompting operates on the principle that as you increase the number of samples per batch, the cost per sample drops significantly. Research and experiments confirm that batch prompting can drastically cut token usage and processing time.
Proof in Practice
Testing across various datasets and with models like Codex, GPT-3.5, and GPT-4 has shown remarkable results. Batch prompting can reduce costs by up to 5x without sacrificing, and sometimes even improving, performance.
Key Takeaways
- Savings: Batch prompting consistently cuts costs in terms of tokens and time.
- Model Flexibility: Its benefits apply across different LLMs, highlighting its wide usability.
- Optimal Batch Size: There's a sweet spot for batch size that maximizes cost efficiency and output quality.
Potential Hurdles and Future Directions
Batch prompting isn't perfect. It might struggle with complex inputs or tasks needing lengthy outputs. Future research will likely refine sample grouping strategies and broaden its application scope.
Conclusion: The Impact of Batch Prompting
Batch prompting stands out as an efficient way to reduce operational costs for LLMs while maintaining or enhancing performance.
It's not just about saving money; it represents a shift towards more efficient, scalable AI applications.
For AI engineers in enterprise settings, adopting batch prompting could unlock new levels of efficiency and innovation. As AI continues to evolve, techniques like batch prompting will be crucial in shaping its future.