NoviCode: Generating Programs from Natural Language Utterances by Novices

• URL: http://arxiv.org/abs/2407.10626v2
• Date: Tue, 16 Jul 2024 05:36:53 GMT
• Title: NoviCode: Generating Programs from Natural Language Utterances by Novices
• Authors: Asaf Achi Mordechai, Yoav Goldberg, Reut Tsarfaty,
• Abstract summary: We present NoviCode, a novel NL Programming task which takes as input an API and a natural language description by a novice non-programmer. We show that NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm.
• Score: 59.71218039095155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current Text-to-Code models demonstrate impressive capabilities in generating executable code from natural language snippets. However, current studies focus on technical instructions and programmer-oriented language, and it is an open question whether these models can effectively translate natural language descriptions given by non-technical users and express complex goals, to an executable program that contains an intricate flow - composed of API access and control structures as loops, conditions, and sequences. To unlock the challenge of generating a complete program from a plain non-technical description we present NoviCode, a novel NL Programming task, which takes as input an API and a natural language description by a novice non-programmer and provides an executable program as output. To assess the efficacy of models on this task, we provide a novel benchmark accompanied by test suites wherein the generated program code is assessed not according to their form, but according to their functional execution. Our experiments show that, first, NoviCode is indeed a challenging task in the code synthesis domain, and that generating complex code from non-technical instructions goes beyond the current Text-to-Code paradigm. Second, we show that a novel approach wherein we align the NL utterances with the compositional hierarchical structure of the code, greatly enhances the performance of LLMs on this task, compared with the end-to-end Text-to-Code counterparts.

Related papers

• UniCoder: Scaling Code Large Language Model via Universal Code [40.248836046285014]
  We introduce the universal code (UniCode) as the intermediate representation. UniCoder-Instruct comprises natural-language questions, code solutions, and the corresponding universal code. The alignment between the intermediate universal code representation and the final code solution significantly improves the quality of the generated code.
  arXiv  Detail & Related papers  (2024-06-24T08:32:48Z)
• Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages [21.18996339478024]
  We introduce emphsynthetic programming elicitation and compilation (SPEAC) SPEAC produces syntactically correct programs more frequently and without sacrificing semantic correctness. We empirically evaluate the performance of SPEAC in a case study for the UCLID5 formal verification language.
  arXiv  Detail & Related papers  (2024-06-05T22:16:19Z)
• 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)
• Code Execution with Pre-trained Language Models [88.04688617516827]
  Most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution. We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension.
  arXiv  Detail & Related papers  (2023-05-08T10:00:05Z)
• A Syntax-Guided Multi-Task Learning Approach for Turducken-Style Code Generation [19.489202790935902]
  We propose a syntax-guided multi-task learning approach TurduckenGen. Specifically, we first explicitly append the type information to the code tokens to capture the representation of syntactic constraints. Then we formalize code generation with syntactic constraint representation as an auxiliary task to enable the model to learn the syntactic constraints of the code.
  arXiv  Detail & Related papers  (2023-03-09T06:22:07Z)
• Python Code Generation by Asking Clarification Questions [57.63906360576212]
  In this work, we introduce a novel and more realistic setup for this task. We hypothesize that the under-specification of a natural language description can be resolved by asking clarification questions. We collect and introduce a new dataset named CodeClarQA containing pairs of natural language descriptions and code with created synthetic clarification questions and answers.
  arXiv  Detail & Related papers  (2022-12-19T22:08:36Z)
• PanGu-Coder: Program Synthesis with Function-Level Language Modeling [47.63943623661298]
  PanGu-Coder is a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation. We train PanGu-Coder using a two-stage strategy: the first stage employs Causal Language Modelling to pre-train on raw programming language data. The second stage uses a combination of Causal Language Modelling and Masked Language Modelling to train on loosely curated pairs of natural language program definitions and code functions.
  arXiv  Detail & Related papers  (2022-07-22T18:08:16Z)
• Using Document Similarity Methods to create Parallel Datasets for Code Translation [60.36392618065203]
  Translating source code from one programming language to another is a critical, time-consuming task. We propose to use document similarity methods to create noisy parallel datasets of code. We show that these models perform comparably to models trained on ground truth for reasonable levels of noise.
  arXiv  Detail & Related papers  (2021-10-11T17:07:58Z)

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.