Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets

• URL: http://arxiv.org/abs/2605.28510v1
• Date: Wed, 27 May 2026 14:12:17 GMT
• Title: Efficient and Scalable Provenance Tracking for LLM-Generated Code Snippets
• Abstract summary: Large language models (LLMs) for code completion and generation may reproduce training examples verbatim and without authorship attribution.<n> Classical fingerprint-based plagiarism detectors based on fingerprinting, such as Winnowing, remain highly effective.<n>We introduce a 300M- parameter encoder tailored for code retrieval, together with a hybrid two-stage provenance-tracking pipeline.
• Score: 3.5312864406384485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) for code completion and generation are increasingly used in software development, yet they may reproduce training examples verbatim and without authorship attribution, raising legal and ethical concerns around plagiarism and license compliance. Classical fingerprint-based plagiarism detectors based on fingerprinting, such as Winnowing, remain highly effective, yet the inspection requires comparing fragments of code to the entire training set, and their linear-time search makes them impractical for the billion-scale corpora used to train modern code LLMs. To bridge this gap, we introduce SOURCETRACKER, a 300M-parameter encoder tailored for code retrieval, together with a hybrid two-stage provenance-tracking pipeline HYBRIDSOURCETRACKER (HST). HST first narrows down a small set of candidate snippets via vector search, then re-ranks those candidates using Winnowing on exact fingerprints. We train and evaluate our system on a 10M-snippet subset of the THESTACKV2 dataset, with both verbatim and adapted snippets that emulate realistic identifier renaming. On an in vitro 100k-snippet search space with adapted queries, our hybrid approach reaches a mean reciprocal rank on par with Winnowing for 30-token fragments. Then, starting from windows >= 60 tokens, it consistently over-performs by up to 5.4% while preserving logarithmic-time query complexity. In a complementary evaluation using an LLM-based judge, we find that many retrieved snippets not labeled as ground truth are still highly similar to the expected sources, particularly with longer context windows, and thus remain useful for end users. Overall, our results demonstrate that integrating vector search with fingerprinting enables scalable, high-precision provenance tracking for code produced by LLMs.

Related papers

• REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling [0.0]
  REIGN is a bi-encoder that operates on sequences of contextualised chunk embeddings from a frozen Guidance Network (GN) rather than on raw tokens.<n>We release a synthetic long-document retrieval benchmark for contrastive training and evaluation at long context lengths.<n>A paired significance test puts it on par with models 1.6-4.3x larger on the patent task, and it stays within 0.65 nDCG@10 of a 20x-larger model on LoCo.
  arXiv  Detail & Related papers  (2026-08-30T16:50:38Z)
• Inferring Code Correctness from Specification [0.0]
  Large language models (LLMs) have become integral to modern software development, enabling automated code generation at scale.<n>We propose TRAILS (Targeted Reasoning Agreement via Inputs and Specifications), an approach that grounds LLM reasoning with concrete (input, output) pairs.<n>We evaluate TRAILS on two datasets, LiveCodeBench and CoCoClaNeL, across three LLMs (Qwen3Coder-30B, Devstral-Small-24B, and Olmo3.1-Instruct), comparing against HoarePrompt and a Zero-Shot Chain-of-Thought baseline.
  arXiv  Detail & Related papers  (2026-05-28T12:04:51Z)
• On the Challenges and Opportunities of Learned Sparse Retrieval for Code [13.929722643500341]
  SPLADE-Code is the first large-scale family of learned sparse retrieval models specialized for code retrieval.<n>We show that SPLADE-Code achieves state-of-the-art performance among retrievers under 1B parameters.
  arXiv  Detail & Related papers  (2026-03-23T14:14:08Z)
• CLARC: C/C++ Benchmark for Robust Code Search [2.225731679677886]
  We present CLARC, a C/C++ benchmark built from real-world GitHub repositories.<n>Clarc contains 1,245 query-code pairs for evaluation and 5,472 pairs for training.
  arXiv  Detail & Related papers  (2026-03-04T18:57:37Z)
• ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding [60.37988508851391]
  Language models (LMs) have become a staple of the code-writing toolbox.<n>Research exploring modifications to Code-LMs' pre-training objectives, geared towards improving data efficiency and better disentangling between syntax and semantics, has been noticeably sparse.<n>In this work, we examine grounding on obfuscated code as a means of helping Code-LMs look beyond the surface-form syntax and enhance their pre-training sample efficiency.
  arXiv  Detail & Related papers  (2025-03-27T23:08:53Z)
• SECRET: Towards Scalable and Efficient Code Retrieval via Segmented Deep Hashing [83.35231185111464]
  Deep learning has shifted the retrieval paradigm from lexical-based matching to encode source code and queries into vector representations.<n>Previous research proposes deep hashing-based methods, which generate hash codes for queries and code snippets and use Hamming distance for rapid recall of code candidates.<n>We propose a novel approach, which converts long hash codes calculated by existing deep hashing approaches into several short hash code segments through an iterative training strategy.
  arXiv  Detail & Related papers  (2024-12-16T12:51:35Z)
• Evaluating and Aligning CodeLLMs on Human Preference [42.26173776584043]
  We present a rigorous human-curated benchmark CodeArena to emulate the complexity and diversity of real-world coding tasks.<n>We also propose a diverse synthetic instruction corpus SynCode-Instruct to verify the effectiveness of the large-scale synthetic instruction fine-tuning.<n>The results find performance differences between execution-based benchmarks and CodeArena.
  arXiv  Detail & Related papers  (2024-12-06T17:40:38Z)
• Nearest Neighbor Speculative Decoding for LLM Generation and Attribution [87.3259169631789]
  Nearest Speculative Decoding (NEST) is capable of incorporating real-world text spans of arbitrary length into the LM generations and providing attribution to their sources.<n>NEST significantly enhances the generation quality and attribution rate of the base LM across a variety of knowledge-intensive tasks.<n>In addition, NEST substantially improves the generation speed, achieving a 1.8x speedup in inference time when applied to Llama-2-Chat 70B.
  arXiv  Detail & Related papers  (2024-05-29T17:55:03Z)
• 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 original code and its LLM-rewritten variants.<n>Our results demonstrate a significant improvement over existing SOTA synthetic content detectors.
  arXiv  Detail & Related papers  (2024-05-25T08:57:28Z)
• Prompt-based Code Completion via Multi-Retrieval Augmented Generation [15.233727939816388]
  ProCC is a code completion framework leveraging prompt engineering and the contextual multi-armed bandits algorithm. ProCC outperforms state-of-the-art code completion technique by 8.6% on our collected open-source benchmark suite. ProCC also allows augmenting fine-tuned techniques in a plug-and-play manner, yielding 5.6% improvement over our studied fine-tuned model.
  arXiv  Detail & Related papers  (2024-05-13T07:56:15Z)
• InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models [56.723509505549536]
  InfiBench is the first large-scale freeform question-answering (QA) benchmark for code to our knowledge. It comprises 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings.
  arXiv  Detail & Related papers  (2024-03-11T02:06:30Z)
• 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)

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.