Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction

The field of programming has a diversity of paradigms that are used according to the working framework. While current neural code generation methods are able to learn and generate code directly from text, we believe that this approach is not optimal for certain code tasks, particularly the generation of classes in an object-oriented project. Specifically, we use natural language processing techniques to extract structured information from requirements descriptions, in order to automate the generation of CRUD (Create, Read, Update, Delete) class code. To facilitate this process, we introduce a pipeline for extracting entity and relation information, as well as a representation called an "Entity Tree" to model this information. We also create a dataset to evaluate the effectiveness of our approach.

A Comprehensive Review of State-of-The-Art Methods for Java Code Generation from Natural Language Text

Java Code Generation consists in generating automatically Java code from a Natural Language Text. This NLP task helps in increasing programmers' productivity by providing them with immediate solutions to the simplest and most repetitive tasks. Code generation is a challenging task because of the hard syntactic rules and the necessity of a deep understanding of the semantic aspect of the programming language. Many works tried to tackle this task using either RNN-based, or Transformer-based models. The latter achieved remarkable advancement in the domain and they can be divided into three groups: (1) encoder-only models, (2) decoder-only models, and (3) encoder-decoder models. In this paper, we provide a comprehensive review of the evolution and progress of deep learning models in Java code generation task. We focus on the most important methods and present their merits and limitations, as well as the objective functions used by the community. In addition, we provide a detailed description of datasets and evaluation metrics used in the literature. Finally, we discuss results of different models on CONCODE dataset, then propose some future directions.

GPT-3.5 vs GPT-4: Evaluating ChatGPT's Reasoning Performance in Zero-shot Learning

Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. However, there is a current hot debate regarding their reasoning capacity. In this paper, we examine the performance of GPT-3.5 and GPT-4 models, by performing a thorough technical evaluation on different reasoning tasks across eleven distinct datasets. Our findings show that GPT-4 outperforms GPT-3.5 in zero-shot learning throughout almost all evaluated tasks. In addition, we note that both models exhibit limited performance in Inductive, Mathematical, and Multi-hop Reasoning Tasks. While it may seem intuitive that the GPT-4 model would outperform GPT-3.5 given its size and efficiency in various NLP tasks, our paper offers empirical evidence to support this claim. We provide a detailed and comprehensive analysis of the results from both models to further support our findings. In addition, we propose a set of engineered prompts that improves performance of both models on zero-shot learning.