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

• URL: http://arxiv.org/abs/2402.10691v4
• Date: Mon, 18 Nov 2024 09:53:03 GMT
• 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,
• Abstract summary: 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. In particular, MultiPoT achieves more than 4.6% improvement on average on ChatGPT (gpt-3.5-turbo-0701)
• Score: 51.49688654641581
• License:
• 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).

Related papers

• CRUXEval-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution [50.7413285637879]
  The CRUXEVAL-X code reasoning benchmark contains 19 programming languages. It comprises at least 600 subjects for each language, along with 19K content-consistent tests in total. Even a model trained solely on Python can achieve at most 34.4% Pass@1 in other languages.
  arXiv  Detail & Related papers  (2024-08-23T11:43:00Z)
• DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages [49.38663048447942]
  We propose DIALECTBENCH, the first-ever large-scale benchmark for NLP on varieties. This allows for a comprehensive evaluation of NLP system performance on different language varieties. We provide substantial evidence of performance disparities between standard and non-standard language varieties.
  arXiv  Detail & Related papers  (2024-03-16T20:18:36Z)
• Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations [59.056367787688146]
  This paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. We construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages. By utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages.
  arXiv  Detail & Related papers  (2023-10-31T08:09:20Z)
• Python Tool for Visualizing Variability of Pareto Fronts over Multiple Runs [1.370633147306388]
  We develop a Python package for empirical attainment surface. The package is available at https://github.com/nabenabe0928/empirical-attainment-func.
  arXiv  Detail & Related papers  (2023-05-15T17:59:34Z)
• ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning [70.57126720079971]
  Large language models (LLMs) have emerged as the most important breakthroughs in natural language processing (NLP) This paper evaluates ChatGPT on 7 different tasks, covering 37 diverse languages with high, medium, low, and extremely low resources. Compared to the performance of previous models, our extensive experimental results demonstrate a worse performance of ChatGPT for different NLP tasks and languages.
  arXiv  Detail & Related papers  (2023-04-12T05:08:52Z)
• Measuring The Impact Of Programming Language Distribution [28.96076723773365]
  We present the BabelCode framework for execution-based evaluation of any benchmark in any language. We present a new code translation dataset called Translating Python Programming Puzzles (TP3) We investigate if balancing the distributions of 14 languages in a training dataset improves a large language model's performance on low-resource languages.
  arXiv  Detail & Related papers  (2023-02-03T19:47:22Z)
• Beyond Static Models and Test Sets: Benchmarking the Potential of Pre-trained Models Across Tasks and Languages [15.373725507698591]
  We argue that this makes the existing practices in multilingual evaluation unreliable and does not provide a full picture of the performance of MMLMs across the linguistic landscape. We propose that the recent work done in Performance Prediction for NLP tasks can serve as a potential solution in fixing benchmarking in Multilingual NLP. We compare performance prediction with translating test data with a case study on four different multilingual datasets, and observe that these methods can provide reliable estimates of the performance that are often on-par with the translation based approaches.
  arXiv  Detail & Related papers  (2022-05-12T20:42:48Z)
• Analysing The Impact Of Linguistic Features On Cross-Lingual Transfer [3.299672391663527]
  We analyze a state-of-the-art multilingual model and try to determine what impacts good transfer between languages. We show that looking at particular syntactic features is 2-4 times more helpful in predicting the performance than an aggregated syntactic similarity.
  arXiv  Detail & Related papers  (2021-05-12T21:22:58Z)
• WARP: Word-level Adversarial ReProgramming [13.08689221166729]
  In many applications it is preferable to tune much smaller sets of parameters, so that the majority of parameters can be shared across multiple tasks. We present an alternative approach based on adversarial reprogramming, which extends earlier work on automatic prompt generation. We show that this approach outperforms other methods with a similar number of trainable parameters on SST-2 and MNLI datasets.
  arXiv  Detail & Related papers  (2021-01-01T00:41:03Z)
• Balancing Training for Multilingual Neural Machine Translation [130.54253367251738]
  multilingual machine translation (MT) models can translate to/from multiple languages. Standard practice is to up-sample less resourced languages to increase representation. We propose a method that instead automatically learns how to weight training data through a data scorer.
  arXiv  Detail & Related papers  (2020-04-14T18:23:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.