A Scalable and Extensible Approach to Benchmarking NL2Code for 18 Programming Languages

• URL: http://arxiv.org/abs/2208.08227v2
• Date: Fri, 19 Aug 2022 01:12:49 GMT
• Title: A Scalable and Extensible Approach to Benchmarking NL2Code for 18 Programming Languages
• Authors: Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, Arjun Guha, Michael Greenberg, Abhinav Jangda
• Abstract summary: We propose MultiPL-E, the first multi-language parallel benchmark for natural-language-to-code-generation. We evaluate two state-of-the-art code generation models on MultiPL-E: Codex and InCoder. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance.
• Score: 1.6312827172331896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models have demonstrated the ability to condition on and generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We facilitate the exploration of this topic by proposing MultiPL-E, the first multi-language parallel benchmark for natural-language-to-code-generation. MultiPL-E extends the HumanEval benchmark (Chen et al, 2021) to support 18 more programming languages, encompassing a range of programming paradigms and popularity. We evaluate two state-of-the-art code generation models on MultiPL-E: Codex and InCoder. We find that on several languages, Codex matches and even exceeds its performance on Python. The range of programming languages represented in MultiPL-E allow us to explore the impact of language frequency and language features on model performance. Finally, the MultiPL-E approach of compiling code generation benchmarks to new programming languages is both scalable and extensible. We describe a general approach for easily adding support for new benchmarks and languages to MultiPL-E.

Related papers

• Multi-Programming Language Ensemble for Code Generation in Large Language Model [5.882816711878273]
  Large language models (LLMs) have significantly improved code generation, particularly in one-pass code generation. Most existing approaches focus solely on generating code in a single programming language, overlooking the potential of leveraging the multi-language capabilities of LLMs. We propose Multi-Programming Language Ensemble (MPLE), a novel ensemble-based method that utilizes code generation across multiple programming languages to enhance overall performance.
  arXiv  Detail & Related papers  (2024-09-06T08:31:18Z)
• Benchmarking LLM Code Generation for Audio Programming with Visual Dataflow Languages [1.559169421643164]
  Node-based programming languages are increasingly popular in media arts coding domains. Using LLM-based code generation to further lower the barrier to creative output is an exciting opportunity. Best strategy for code generation for visual node-based programming languages is still an open question.
  arXiv  Detail & Related papers  (2024-09-01T22:11:23Z)
• 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)
• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• Can Large Language Models Write Parallel Code? [0.5317767988097261]
  Large language models are increasingly becoming a popular tool for software development. In this paper, we study the capabilities of state-of-the-art language models to generate parallel code.
  arXiv  Detail & Related papers  (2024-01-23T08:25:12Z)
• Multi-lingual Evaluation of Code Generation Models [82.7357812992118]
  We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. These datasets cover over 10 programming languages. We are able to assess the performance of code generation models in a multi-lingual fashion.
  arXiv  Detail & Related papers  (2022-10-26T17:17:06Z)
• MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages [76.93265104421559]
  We benchmark code generation from natural language commands extending beyond English. We annotated a total of 896 NL-code pairs in three languages: Spanish, Japanese, and Russian. While the difficulties vary across these three languages, all systems lag significantly behind their English counterparts.
  arXiv  Detail & Related papers  (2022-03-16T04:21:50Z)
• A Systematic Evaluation of Large Language Models of Code [88.34057460577957]
  Large language models (LMs) of code have recently shown tremendous promise in completing code and synthesizing code from natural language descriptions. The current state-of-the-art code LMs are not publicly available, leaving many questions about their model and data design decisions. Although Codex is not open-source, we find that existing open-source models do achieve close results in some programming languages. We release a new model, PolyCoder, with 2.7B parameters based on the GPT-2 architecture, which was trained on 249GB of code across 12 programming languages on a single machine.
  arXiv  Detail & Related papers  (2022-02-26T15:53:55Z)
• X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models [103.75890012041366]
  Language models (LMs) have proven surprisingly successful at capturing factual knowledge. However, studies on LMs' factual representation ability have almost invariably been performed on English. We create a benchmark of cloze-style probes for 23 typologically diverse languages.
  arXiv  Detail & Related papers  (2020-10-13T05:29:56Z)

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.