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        <title>Batch Prompting: Efficient Inference with Large Language Model APIs</title>
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    <name type="personal">
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track</title>
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            <namePart type="given">Mingxuan</namePart>
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    <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 5\times 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 is released at the site https://github.com/xlang-ai/batch-prompting.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-industry.74</identifier>
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%0 Conference Proceedings
%T Batch Prompting: Efficient Inference with Large Language Model APIs
%A Cheng, Zhoujun
%A Kasai, Jungo
%A Yu, Tao
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F cheng-etal-2023-batch
%X 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 5\times 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 is released at the site https://github.com/xlang-ai/batch-prompting.
%R 10.18653/v1/2023.emnlp-industry.74
%U https://aclanthology.org/2023.emnlp-industry.74
%U https://doi.org/10.18653/v1/2023.emnlp-industry.74
%P 792-810

Markdown (Informal)

[Batch Prompting: Efficient Inference with Large Language Model APIs](https://aclanthology.org/2023.emnlp-industry.74) (Cheng et al., EMNLP 2023)

• Batch Prompting: Efficient Inference with Large Language Model APIs (Cheng et al., EMNLP 2023)

ACL

• Zhoujun Cheng, Jungo Kasai, and Tao Yu. 2023. Batch Prompting: Efficient Inference with Large Language Model APIs. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 792–810, Singapore. Association for Computational Linguistics.