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Computer Science > Computation and Language

arXiv:2402.10691 (cs)
[Submitted on 16 Feb 2024 (v1), last revised 18 Nov 2024 (this version, v4)]

Title:Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts

Authors:Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
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Abstract:Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. Currently, PoT primarily uses Python. However, relying solely on a single language may result in suboptimal solutions and overlook the potential benefits of other programming languages. In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models. The effectiveness of each language varies depending on the specific scenarios. Inspired by this, we propose a task and model agnostic approach called MultiPoT, which harnesses strength and diversity from various languages. Experimental results reveal that it significantly outperforms Python Self-Consistency. Furthermore, it achieves comparable or superior performance compared to the best monolingual PoT in almost all tasks across all models. In particular, MultiPoT achieves more than 4.6% improvement on average on ChatGPT (gpt-3.5-turbo-0701).
Comments: Accepted by EMNLP 2024. Code and data are released at this https URL
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2402.10691 [cs.CL]
  (or arXiv:2402.10691v4 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2402.10691

Submission history

From: Xianzhen Luo [view email]
[v1] Fri, 16 Feb 2024 13:48:06 UTC (8,323 KB)
[v2] Sun, 16 Jun 2024 12:29:50 UTC (8,245 KB)
[v3] Fri, 20 Sep 2024 02:54:30 UTC (8,245 KB)
[v4] Mon, 18 Nov 2024 09:53:03 UTC (8,231 KB)
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