Distinguishing LLM-generated from Human-written Code by Contrastive Learning

• URL: http://arxiv.org/abs/2411.04704v1
• Date: Thu, 07 Nov 2024 13:39:14 GMT
• Title: Distinguishing LLM-generated from Human-written Code by Contrastive Learning
• Authors: Xiaodan Xu, Chao Ni, Xinrong Guo, Shaoxuan Liu, Xiaoya Wang, Kui Liu, Xiaohu Yang,
• Abstract summary: Large language models (LLMs) have attracted significant attention due to their demonstrated ability to generate high-quality content for various tasks. There are growing concerns regarding their potential risks in various fields, such as news, education, and software engineering. This paper proposes a novel ChatGPT-generated code detector, CodeGPTSensor, based on a contrastive learning framework and a semantic encoder built with UniXcoder.
• Score: 5.553326595990857
• License:
• Abstract: Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted significant attention from both industry and academia due to their demonstrated ability to generate high-quality content for various tasks. Despite the impressive capabilities of LLMs, there are growing concerns regarding their potential risks in various fields, such as news, education, and software engineering. Recently, several commercial and open-source LLM-generated content detectors have been proposed, which, however, are primarily designed for detecting natural language content without considering the specific characteristics of program code. This paper aims to fill this gap by proposing a novel ChatGPT-generated code detector, CodeGPTSensor, based on a contrastive learning framework and a semantic encoder built with UniXcoder. To assess the effectiveness of CodeGPTSensor on differentiating ChatGPT-generated code from human-written code, we first curate a large-scale Human and Machine comparison Corpus (HMCorp), which includes 550K pairs of human-written and ChatGPT-generated code (i.e., 288K Python code pairs and 222K Java code pairs). Based on the HMCorp dataset, our qualitative and quantitative analysis of the characteristics of ChatGPT-generated code reveals the challenge and opportunity of distinguishing ChatGPT-generated code from human-written code with their representative features. Our experimental results indicate that CodeGPTSensor can effectively identify ChatGPT-generated code, outperforming all selected baselines.

Related papers

• Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting [78.48355455324688]
  We propose a novel zero-shot synthetic code detector based on the similarity between the code and its rewritten variants. Our results demonstrate a notable enhancement over existing synthetic content detectors designed for general texts.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• CodeIP: A Grammar-Guided Multi-Bit Watermark for Large Language Models of Code [56.019447113206006]
  Large Language Models (LLMs) have achieved remarkable progress in code generation. CodeIP is a novel multi-bit watermarking technique that embeds additional information to preserve provenance details. Experiments conducted on a real-world dataset across five programming languages demonstrate the effectiveness of CodeIP.
  arXiv  Detail & Related papers  (2024-04-24T04:25:04Z)
• Assured LLM-Based Software Engineering [51.003878077888686]
  This paper is an outline of the content of the keynote by Mark Harman at the International Workshop on Interpretability, Robustness, and Benchmarking in Neural Software Engineering, Monday 15th April 2024, Lisbon, Portugal.
  arXiv  Detail & Related papers  (2024-02-06T20:38:46Z)
• DEMASQ: Unmasking the ChatGPT Wordsmith [63.8746084667206]
  We propose an effective ChatGPT detector named DEMASQ, which accurately identifies ChatGPT-generated content. Our method addresses two critical factors: (i) the distinct biases in text composition observed in human- and machine-generated content and (ii) the alterations made by humans to evade previous detection methods.
  arXiv  Detail & Related papers  (2023-11-08T21:13:05Z)
• Assessing the Promise and Pitfalls of ChatGPT for Automated Code Generation [2.0400340435492272]
  This paper presents a comprehensive evaluation of the code generation capabilities of ChatGPT, a prominent large language model. A dataset of 131 code-generation prompts across 5 categories was curated to enable robust analysis. Code solutions were generated by both ChatGPT and humans for all prompts, resulting in 262 code samples.
  arXiv  Detail & Related papers  (2023-11-05T12:56:40Z)
• CodeFuse-13B: A Pretrained Multi-lingual Code Large Language Model [58.127534002232096]
  This paper introduces CodeFuse-13B, an open-sourced pre-trained code LLM. It is specifically designed for code-related tasks with both English and Chinese prompts. CodeFuse achieves its effectiveness by utilizing a high quality pre-training dataset.
  arXiv  Detail & Related papers  (2023-10-10T02:38:44Z)
• Zero-Shot Detection of Machine-Generated Codes [83.0342513054389]
  This work proposes a training-free approach for the detection of LLMs-generated codes. We find that existing training-based or zero-shot text detectors are ineffective in detecting code. Our method exhibits robustness against revision attacks and generalizes well to Java codes.
  arXiv  Detail & Related papers  (2023-10-08T10:08:21Z)
• No Need to Lift a Finger Anymore? Assessing the Quality of Code Generation by ChatGPT [28.68768157452352]
  This study examines the quality of code generation using ChatGPT. We leverage 728 algorithm problems in five languages (i.e., C, C++, Java, Python, and JavaScript) and 18 CWEs with 54 code scenarios for the code generation task. Our findings uncover potential issues and limitations that arise in the ChatGPT-based code generation.
  arXiv  Detail & Related papers  (2023-08-09T10:01:09Z)
• Discriminating Human-authored from ChatGPT-Generated Code Via Discernable Feature Analysis [2.9398911304923447]
  This paper specifically aims to distinguish code generated by ChatGPT from that authored by humans. We devise a dataset cleansing technique, which employs temporal and spatial segmentation, to mitigate the dearth of datasets. To further enrich data resources, we employ "code transformation," "feature transformation," and "feature customization" techniques, generating an extensive dataset comprising 10,000 lines of ChatGPT-generated code.
  arXiv  Detail & Related papers  (2023-06-26T03:15:06Z)
• Automatic Code Summarization via ChatGPT: How Far Are We? [10.692654700225411]
  We evaluate ChatGPT on a widely-used Python dataset called CSN-Python. In terms of BLEU and ROUGE-L, ChatGPT's code summarization performance is significantly worse than all three SOTA models. Based on the findings, we outline several open challenges and opportunities in ChatGPT-based code summarization.
  arXiv  Detail & Related papers  (2023-05-22T09:43:40Z)

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.