36.3681, Confs: SemEval-2026 Workshop Task 13: Detecting Machine-Generated Code (Online)

[The LINGUIST List]

LINGUIST List: Vol-36-3681. Fri Nov 28 2025. ISSN: 1069 - 4875.

Subject: 36.3681, Confs: SemEval-2026 Workshop Task 13: Detecting Machine-Generated Code (Online)

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Date: 27-Nov-2025
From: Preslav Nakov [preslav.nakov at gmail.com]
Subject: SemEval-2026 Workshop Task 13: Detecting Machine-Generated Code


SemEval-2026 Workshop Task 13: Detecting Machine-Generated Code
Short Title: SemEval-2026 Task 13

Location: Online
Meeting URL: https://github.com/mbzuai-nlp/SemEval-2026-Task13

Linguistic Field(s): Computational Linguistics

Call for participation:
SemEval-2026 Task 13: Detecting Machine-Generated Code
We announce an exciting and challenging new SemEval task!
With the rapid growth of large language models for code generation, it
is becoming increasingly difficult to distinguish between code written
by humans and code produced by AI systems. This raises serious
concerns for academic integrity, hiring evaluations, and software
security.
To address this, we are introducing SemEval-2026 Task 13: Detecting
Machine-Generated Code with Multiple Programming Languages,
Generators, and Application Scenarios. It is focused on building
systems that can tell whether code was authored by humans or by LLMs.
The task includes three subtasks:
Subtask A: Binary Classification (Human vs. Machine)
https://www.kaggle.com/t/99673e23fe8546cf9a07a40f36f2cc7e
A real-world challenge with strong out-of-distribution shifts in the
test set. Participants will need to design robust models capable of
adapting to unseen programming languages and contexts: for instance,
determining whether a system trained on competitive programming data
can generalize to identifying LLM-authore code in GitHub repositories.
Subtask B: Multiclass LLM Authorship Identification
https://www.kaggle.com/t/65af9e22be6d43d884cfd6e41cad3ee4
Participants must identify which specific LLM produced a given code
snippet. This subtask aims to explore how stylistic and structural
differences across models can support source attribution.
Subtask C: Four-Way Hybrid Authorship Classification
https://www.kaggle.com/t/005ab8234f27424aa096b7c00a073722
A 4-class classification task task. We ask participants to build a
system which can not only identify fully haman or LLM-generated codes,
but also to find codes with hybrid authorship (human written and then
LLM adjusted) and more seriously, codes that were generated by LLMs in
attempt to fool detectors (either by prompting or by RLHF
fine-tuning).
If you're interested in tackling one of the most relevant and
high-impact challenges in code forensics and AI transparency, explore
our GitHub repository:
https://github.com/mbzuai-nlp/SemEval-2026-Task13



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