Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting

Tong Ye, Yangkai Du, Tengfei Ma, Lingfei Wu, Xuhong Zhang, Shouling Ji, Wenhai Wang·May 25, 2024

Summary

This research paper investigates a zero-shot synthetic code detector for large language models (LLMs) in the context of code generation, addressing concerns about academic dishonesty and security risks. The proposed method employs code rewriting and self-supervised contrastive learning to identify similarities between original and rewritten code, assuming that LLM-generated code will exhibit more consistent patterns. The authors achieve significant improvements in detection, with a 20.5% increase in AUROC on the APPS benchmark and a 29.1% increase on MBPP, compared to existing text-based detectors. The study highlights the need for specialized tools for LLM-generated code and contributes a novel approach, along with publicly available resources for further research. It also compares various LLMs and detection methods, emphasizing the challenges and opportunities in distinguishing human-written from machine-generated code.

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of developing a synthetic code detector to detect the misuse of Large Language Models (LLMs) generated code, specifically focusing on code content . This problem is not entirely new, as there have been previous efforts to detect LLM-generated pure text, but the focus on code content is a novel aspect of this research . The study reveals that existing state-of-the-art detection methods, designed for general texts, face significant challenges when applied to code content due to the unique grammatical structure of programming languages and the presence of "low-entropy" tokens in code .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that existing state-of-the-art text detection methods, which rely on the statistical log probability of tokens, exhibit a significant decline in performance when applied to the domain of code content due to the uniform grammatical structure and deterministic nature of code tokens, leading to challenges in differentiating between human-written code and model-generated code . The study investigates the reasons for this discrepancy and proposes a novel detection approach that utilizes code rewriting and similarity measurement to effectively address the unique challenges of synthetic code detection .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" proposes several new ideas, methods, and models in the field of code generation and detection :

  1. StarCoder: The paper introduces StarCoder, a coding assistant created by Lewis Tunstall, Nathan Lambert, and others, which utilizes Open Foundation and Fine-Tuned Chat Models .

  2. SynCoBERT and CODE-MVP: The paper discusses SynCoBERT, a Syntax-Guided Multi-Modal Contrastive Pre-Training model for code representation, and CODE-MVP, a model for learning to represent source code from multiple views with contrastive pre-training .

  3. Codet5 and Incoder: The paper presents Codet5, an Identifier-aware unified pre-trained encoder-decoder model for code understanding and generation, and Incoder, a generative model for code infilling and synthesis .

  4. DetectGPT and Detectllm: The paper introduces DetectGPT, a model for zero-shot machine-generated text detection using Probability Curvature, and Detectllm, a system leveraging log rank information for zero-shot detection of machine-generated text .

  5. GraphCode{BERT} and UniXcoder: The paper discusses GraphCode{BERT}, a model for pre-training code representations with data flow, and UniXcoder, a unified cross-modal pre-training model for code representation .

  6. PaLM and CodeBERT: The paper introduces PaLM, a model for scaling language modeling with pathways, and CodeBERT, a pre-trained model for programming and natural languages .

  7. Chain-of-Thought Prompting and Ro-bust Multi-bit Natural Language Watermarking: The paper presents Chain-of-Thought Prompting for eliciting reasoning in large language models, and Ro-bust Multi-bit Natural Language Watermarking through Invariant Features .

These models and methods contribute to advancements in code generation, code understanding, text detection, pre-training for code representation, and security hardening in large language models. The paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" introduces novel characteristics and advantages compared to previous methods in synthetic code detection:

  1. Holistic Approach: The proposed method utilizes a holistic perspective for detecting synthetic code, focusing on code rewriting and similarity measurement rather than token-wise scores. This approach effectively overcomes the limitations of previous methods when applied to the code domain .

  2. Performance Improvement: The new method significantly outperforms state-of-the-art methods, demonstrating a 20.5% improvement in detection performance on the APPS dataset and a 29.1% improvement on the MBPP dataset. This improvement is attributed to the innovative detection approach that addresses the unique challenges of synthetic code detection .

  3. Universal Applicability: The proposed zero-shot synthetic code detector is applicable to both open-sourced code Language Model Models (LLMs) and closed-source LLMs like ChatGPT/GPT-4, which only offer APIs. This universal applicability enhances the versatility and effectiveness of the detection method .

  4. Resource Efficiency: The new method requires minimal resources and permissions, only necessitating the capability to perform LLM inference or access to their APIs. This contrasts with previous detection methods that required knowledge of token log probabilities, making the proposed method more practical and resource-efficient .

  5. Generalizability: The method's effectiveness extends to different programming languages, as demonstrated by notable improvements in detection performance on a C++ benchmark. The method outperforms other zero-shot baselines on C++ benchmarks, indicating its generalizability across programming languages .

  6. Consistency and Robustness: The proposed method exhibits superior consistency across different temperatures, enhancing its robustness in detecting synthetic code. Additionally, the method shows consistent performance improvements with the number of code rewrites, indicating its reliability and stability .

Overall, the paper's method offers a comprehensive and effective approach to synthetic code detection, addressing the unique challenges of the code domain, achieving significant performance improvements, ensuring universal applicability, resource efficiency, generalizability to different programming languages, and demonstrating consistency and robustness in detection tasks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of Large Language Models (LLMs) and code generation. Noteworthy researchers in this field include authors such as Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton, Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, Arthur Szlam, Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang, Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Kathy Meier-Hellstern, Douglas Eck, Slav Petrov, Noah Fiedel, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, among others .

The key to the solution mentioned in the paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" is a novel zero-shot synthetic code detector based on the similarity between the code and its rewritten variants. This method relies on the intuition that the differences between the LLM-rewritten and original codes tend to be smaller when the original code is synthetic. The approach utilizes self-supervised contrastive learning to train a code similarity model and has shown significant improvements over current synthetic content detectors designed for general texts .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of zero-shot synthetic code detectors via code rewriting and similarity measurement . The experiments aimed to address the gap in applying synthetic content detectors designed for general texts to code content . The study proposed a novel detection approach that effectively tackled the unique challenges of synthetic code detection by utilizing code rewriting and similarity measurement . The experiments included testing the detection performance on different code distributions and comparing it to supervised detectors . Additionally, ablation studies were conducted to analyze the contributions of two primary components in the design: Code Rewriting and Similarity Measurement . The experiments were comprehensive, covering various factors such as the choice of Similarity Model, impact of generation prompts, decoding strategy, generalizability to different programming languages, detection of revised synthetic code, consideration of code correctness, and assessment of code length impact .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is constructed utilizing two synthetic code detection datasets: APPS and MBPP . The code, dataset, and trained code similarity model checkpoint are made publicly available . This means that the code used for the evaluation is open source and accessible for further research and analysis.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study identified a performance gap in applying synthetic content detectors designed for general texts to the code domain and proposed a novel detection approach that effectively addresses the unique challenges of synthetic code detection . The experiments revealed a significant decline in detection performance when state-of-the-art methods for text detection were applied to the code domain, highlighting the need for specialized detection methods for code content . Additionally, the proposed zero-shot synthetic code detector demonstrated notable improvements in accuracy and robustness compared to existing methods, showcasing the effectiveness of the new approach . The results showed that the method consistently outperformed other detectors across various scenarios, indicating its efficacy in detecting synthetic code . Furthermore, the experiments conducted with different detection LLMs and generation LLMs provided valuable insights into the performance variations based on model compatibility, contributing to a comprehensive analysis of the detection methods .


What are the contributions of this paper?

The paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" makes several contributions in the field of code generation and language models :

  • It discusses the development of coding assistants using large language models (LLMs) like GPT-4, which offer intelligent code completion and document generation capabilities for programmers .
  • The paper highlights the efficiency improvements in the coding process and the lowered entry barrier to programming due to LLMs, while also addressing concerns about the potential misuse of LLM-generated code, particularly in educational settings .
  • It explores the use of LLMs by students for writing solutions in coding assignments and exams, showcasing how LLMs like GPT-4 can achieve human-level performance in solving coding problems .
  • The research delves into the demand for code security in industrial applications, pointing out that LLM-generated code may contain security vulnerabilities, as highlighted in an evaluation study .
  • Additionally, the paper discusses the advancements in instruction tuning, AI alignment research, and the development of general-purpose conversational LLMs, such as GPT-4, which can provide high-quality responses to general human requests, including generating code implementations based on detailed coding specifications .

What work can be continued in depth?

Further research can be conducted to enhance the detection of synthetic code generated by Large Language Models (LLMs) . Specifically, there is a need to focus on developing more effective methods for detecting LLM-generated code content, as existing detection techniques designed for general texts show a significant decline in performance when applied to code domain . This research could involve exploring novel approaches that take into account the unique characteristics of code, such as its uniform grammatical structure and deterministic nature in specific programming languages . By addressing these challenges, advancements can be made in accurately differentiating between human-written code and model-generated code, thereby improving the overall security and integrity of codebases in various applications .


Introduction
Background
Academic dishonesty and security risks in code generation
Growing reliance on LLMs for code generation
Objective
To develop a specialized tool for LLM-generated code detection
Improve upon existing text-based detectors
Address the need for effective code authenticity verification
Method
Code Rewriting and Self-Supervised Learning
Code Rewriting Strategy
Transformation techniques for creating variations of original code
Contrastive Learning Approach
Identifying similarities between original and rewritten code
Consistency patterns in machine-generated code
Data Collection
APPS and MBPP benchmark datasets for evaluation
Human-written and LLM-generated code samples
Data Preprocessing
Cleaning and formatting of code data
Splitting datasets for training and testing
Model Development
Design and implementation of the zero-shot code detector
Comparison of different LLMs in the context of detection
Evaluation
AUROC improvements on APPS and MBPP benchmarks
Performance metrics and analysis
Results and Discussion
Detection accuracy enhancements compared to existing methods
Publicly available resources for further research
Challenges and opportunities in distinguishing human-written from machine-generated code
Conclusion
Significance of the proposed detector for academic integrity and security
Future directions for enhancing LLM-generated code detection
Implications for the broader AI community and code generation practices
Basic info
papers
software engineering
artificial intelligence
Advanced features
Insights
What technique does the proposed method use to detect synthetic code?
What are the main findings and contributions of the study regarding LLM-generated code and academic dishonesty?
How do the authors measure the effectiveness of their code detector, and what improvements do they report?
What is the primary focus of the research paper?

Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting

Tong Ye, Yangkai Du, Tengfei Ma, Lingfei Wu, Xuhong Zhang, Shouling Ji, Wenhai Wang·May 25, 2024

Summary

This research paper investigates a zero-shot synthetic code detector for large language models (LLMs) in the context of code generation, addressing concerns about academic dishonesty and security risks. The proposed method employs code rewriting and self-supervised contrastive learning to identify similarities between original and rewritten code, assuming that LLM-generated code will exhibit more consistent patterns. The authors achieve significant improvements in detection, with a 20.5% increase in AUROC on the APPS benchmark and a 29.1% increase on MBPP, compared to existing text-based detectors. The study highlights the need for specialized tools for LLM-generated code and contributes a novel approach, along with publicly available resources for further research. It also compares various LLMs and detection methods, emphasizing the challenges and opportunities in distinguishing human-written from machine-generated code.
Mind map
Consistency patterns in machine-generated code
Identifying similarities between original and rewritten code
Transformation techniques for creating variations of original code
Performance metrics and analysis
AUROC improvements on APPS and MBPP benchmarks
Comparison of different LLMs in the context of detection
Design and implementation of the zero-shot code detector
Splitting datasets for training and testing
Cleaning and formatting of code data
Human-written and LLM-generated code samples
APPS and MBPP benchmark datasets for evaluation
Contrastive Learning Approach
Code Rewriting Strategy
Address the need for effective code authenticity verification
Improve upon existing text-based detectors
To develop a specialized tool for LLM-generated code detection
Growing reliance on LLMs for code generation
Academic dishonesty and security risks in code generation
Implications for the broader AI community and code generation practices
Future directions for enhancing LLM-generated code detection
Significance of the proposed detector for academic integrity and security
Challenges and opportunities in distinguishing human-written from machine-generated code
Publicly available resources for further research
Detection accuracy enhancements compared to existing methods
Evaluation
Model Development
Data Preprocessing
Data Collection
Code Rewriting and Self-Supervised Learning
Objective
Background
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Academic dishonesty and security risks in code generation
Growing reliance on LLMs for code generation
Objective
To develop a specialized tool for LLM-generated code detection
Improve upon existing text-based detectors
Address the need for effective code authenticity verification
Method
Code Rewriting and Self-Supervised Learning
Code Rewriting Strategy
Transformation techniques for creating variations of original code
Contrastive Learning Approach
Identifying similarities between original and rewritten code
Consistency patterns in machine-generated code
Data Collection
APPS and MBPP benchmark datasets for evaluation
Human-written and LLM-generated code samples
Data Preprocessing
Cleaning and formatting of code data
Splitting datasets for training and testing
Model Development
Design and implementation of the zero-shot code detector
Comparison of different LLMs in the context of detection
Evaluation
AUROC improvements on APPS and MBPP benchmarks
Performance metrics and analysis
Results and Discussion
Detection accuracy enhancements compared to existing methods
Publicly available resources for further research
Challenges and opportunities in distinguishing human-written from machine-generated code
Conclusion
Significance of the proposed detector for academic integrity and security
Future directions for enhancing LLM-generated code detection
Implications for the broader AI community and code generation practices

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of developing a synthetic code detector to detect the misuse of Large Language Models (LLMs) generated code, specifically focusing on code content . This problem is not entirely new, as there have been previous efforts to detect LLM-generated pure text, but the focus on code content is a novel aspect of this research . The study reveals that existing state-of-the-art detection methods, designed for general texts, face significant challenges when applied to code content due to the unique grammatical structure of programming languages and the presence of "low-entropy" tokens in code .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that existing state-of-the-art text detection methods, which rely on the statistical log probability of tokens, exhibit a significant decline in performance when applied to the domain of code content due to the uniform grammatical structure and deterministic nature of code tokens, leading to challenges in differentiating between human-written code and model-generated code . The study investigates the reasons for this discrepancy and proposes a novel detection approach that utilizes code rewriting and similarity measurement to effectively address the unique challenges of synthetic code detection .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" proposes several new ideas, methods, and models in the field of code generation and detection :

  1. StarCoder: The paper introduces StarCoder, a coding assistant created by Lewis Tunstall, Nathan Lambert, and others, which utilizes Open Foundation and Fine-Tuned Chat Models .

  2. SynCoBERT and CODE-MVP: The paper discusses SynCoBERT, a Syntax-Guided Multi-Modal Contrastive Pre-Training model for code representation, and CODE-MVP, a model for learning to represent source code from multiple views with contrastive pre-training .

  3. Codet5 and Incoder: The paper presents Codet5, an Identifier-aware unified pre-trained encoder-decoder model for code understanding and generation, and Incoder, a generative model for code infilling and synthesis .

  4. DetectGPT and Detectllm: The paper introduces DetectGPT, a model for zero-shot machine-generated text detection using Probability Curvature, and Detectllm, a system leveraging log rank information for zero-shot detection of machine-generated text .

  5. GraphCode{BERT} and UniXcoder: The paper discusses GraphCode{BERT}, a model for pre-training code representations with data flow, and UniXcoder, a unified cross-modal pre-training model for code representation .

  6. PaLM and CodeBERT: The paper introduces PaLM, a model for scaling language modeling with pathways, and CodeBERT, a pre-trained model for programming and natural languages .

  7. Chain-of-Thought Prompting and Ro-bust Multi-bit Natural Language Watermarking: The paper presents Chain-of-Thought Prompting for eliciting reasoning in large language models, and Ro-bust Multi-bit Natural Language Watermarking through Invariant Features .

These models and methods contribute to advancements in code generation, code understanding, text detection, pre-training for code representation, and security hardening in large language models. The paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" introduces novel characteristics and advantages compared to previous methods in synthetic code detection:

  1. Holistic Approach: The proposed method utilizes a holistic perspective for detecting synthetic code, focusing on code rewriting and similarity measurement rather than token-wise scores. This approach effectively overcomes the limitations of previous methods when applied to the code domain .

  2. Performance Improvement: The new method significantly outperforms state-of-the-art methods, demonstrating a 20.5% improvement in detection performance on the APPS dataset and a 29.1% improvement on the MBPP dataset. This improvement is attributed to the innovative detection approach that addresses the unique challenges of synthetic code detection .

  3. Universal Applicability: The proposed zero-shot synthetic code detector is applicable to both open-sourced code Language Model Models (LLMs) and closed-source LLMs like ChatGPT/GPT-4, which only offer APIs. This universal applicability enhances the versatility and effectiveness of the detection method .

  4. Resource Efficiency: The new method requires minimal resources and permissions, only necessitating the capability to perform LLM inference or access to their APIs. This contrasts with previous detection methods that required knowledge of token log probabilities, making the proposed method more practical and resource-efficient .

  5. Generalizability: The method's effectiveness extends to different programming languages, as demonstrated by notable improvements in detection performance on a C++ benchmark. The method outperforms other zero-shot baselines on C++ benchmarks, indicating its generalizability across programming languages .

  6. Consistency and Robustness: The proposed method exhibits superior consistency across different temperatures, enhancing its robustness in detecting synthetic code. Additionally, the method shows consistent performance improvements with the number of code rewrites, indicating its reliability and stability .

Overall, the paper's method offers a comprehensive and effective approach to synthetic code detection, addressing the unique challenges of the code domain, achieving significant performance improvements, ensuring universal applicability, resource efficiency, generalizability to different programming languages, and demonstrating consistency and robustness in detection tasks.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research papers exist in the field of Large Language Models (LLMs) and code generation. Noteworthy researchers in this field include authors such as Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, Charles Sutton, Anton Bakhtin, Sam Gross, Myle Ott, Yuntian Deng, Marc’Aurelio Ranzato, Arthur Szlam, Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang, Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Kathy Meier-Hellstern, Douglas Eck, Slav Petrov, Noah Fiedel, Felipe Petroski Such, Natalie Summers, Ilya Sutskever, Jie Tang, Nikolas Tezak, Madeleine B. Thompson, Phil Tillet, Amin Tootoonchian, Elizabeth Tseng, Preston Tuggle, Nick Turley, Juan Felipe Cerón Uribe, Andrea Vallone, Arun Vijayvergiya, Chelsea Voss, Carroll Wainwright, Justin Jay Wang, Alvin Wang, Ben Wang, Jonathan Ward, CJ Weinmann, Akila Welihinda, Peter Welinder, Jiayi Weng, Lilian Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, among others .

The key to the solution mentioned in the paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" is a novel zero-shot synthetic code detector based on the similarity between the code and its rewritten variants. This method relies on the intuition that the differences between the LLM-rewritten and original codes tend to be smaller when the original code is synthetic. The approach utilizes self-supervised contrastive learning to train a code similarity model and has shown significant improvements over current synthetic content detectors designed for general texts .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of zero-shot synthetic code detectors via code rewriting and similarity measurement . The experiments aimed to address the gap in applying synthetic content detectors designed for general texts to code content . The study proposed a novel detection approach that effectively tackled the unique challenges of synthetic code detection by utilizing code rewriting and similarity measurement . The experiments included testing the detection performance on different code distributions and comparing it to supervised detectors . Additionally, ablation studies were conducted to analyze the contributions of two primary components in the design: Code Rewriting and Similarity Measurement . The experiments were comprehensive, covering various factors such as the choice of Similarity Model, impact of generation prompts, decoding strategy, generalizability to different programming languages, detection of revised synthetic code, consideration of code correctness, and assessment of code length impact .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is constructed utilizing two synthetic code detection datasets: APPS and MBPP . The code, dataset, and trained code similarity model checkpoint are made publicly available . This means that the code used for the evaluation is open source and accessible for further research and analysis.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed verification. The study identified a performance gap in applying synthetic content detectors designed for general texts to the code domain and proposed a novel detection approach that effectively addresses the unique challenges of synthetic code detection . The experiments revealed a significant decline in detection performance when state-of-the-art methods for text detection were applied to the code domain, highlighting the need for specialized detection methods for code content . Additionally, the proposed zero-shot synthetic code detector demonstrated notable improvements in accuracy and robustness compared to existing methods, showcasing the effectiveness of the new approach . The results showed that the method consistently outperformed other detectors across various scenarios, indicating its efficacy in detecting synthetic code . Furthermore, the experiments conducted with different detection LLMs and generation LLMs provided valuable insights into the performance variations based on model compatibility, contributing to a comprehensive analysis of the detection methods .


What are the contributions of this paper?

The paper "Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting" makes several contributions in the field of code generation and language models :

  • It discusses the development of coding assistants using large language models (LLMs) like GPT-4, which offer intelligent code completion and document generation capabilities for programmers .
  • The paper highlights the efficiency improvements in the coding process and the lowered entry barrier to programming due to LLMs, while also addressing concerns about the potential misuse of LLM-generated code, particularly in educational settings .
  • It explores the use of LLMs by students for writing solutions in coding assignments and exams, showcasing how LLMs like GPT-4 can achieve human-level performance in solving coding problems .
  • The research delves into the demand for code security in industrial applications, pointing out that LLM-generated code may contain security vulnerabilities, as highlighted in an evaluation study .
  • Additionally, the paper discusses the advancements in instruction tuning, AI alignment research, and the development of general-purpose conversational LLMs, such as GPT-4, which can provide high-quality responses to general human requests, including generating code implementations based on detailed coding specifications .

What work can be continued in depth?

Further research can be conducted to enhance the detection of synthetic code generated by Large Language Models (LLMs) . Specifically, there is a need to focus on developing more effective methods for detecting LLM-generated code content, as existing detection techniques designed for general texts show a significant decline in performance when applied to code domain . This research could involve exploring novel approaches that take into account the unique characteristics of code, such as its uniform grammatical structure and deterministic nature in specific programming languages . By addressing these challenges, advancements can be made in accurately differentiating between human-written code and model-generated code, thereby improving the overall security and integrity of codebases in various applications .

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