Work

mcdok at SemEval-2026 Task 13: Finetuning LLMs for Detection of Machine-Generated Code

Year: 2026

Type: conference-paper

Abstract: Multi-domain detection of the machinegenerated code snippets in various programming languages is a challenging task.SemEval-2026 Task 13 copes with this challenge in various angles, as a binary detection problem as well as attribution of the source.Specifically, its subtasks also cover generator LLM family detection, as well as a hybrid code co-generated by humans and machines, or adversarially modified codes hiding its origin.Our submitted systems adjusted the existing mdok approach (focused on machine-generated text detection) to these specific kinds of problems by exploring various base models, more suitable for code understanding.The results indicate that the submitted systems are competitive in all three subtasks.However, the margins from the topperforming systems are significant, and thus further improvements are possible.

Source: International Workshop on Semantic Evaluation (SemEval)

DOI: 10.18653/v1/2026.semeval-1.47

Authors: Adam Skurla, Dominik Macko, Jakub Simko

Institutions: Kempelen Institute of Intelligent Technologies, Brno University of Technology

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: European Commission, National Supercomputing Center, Korea Institute of Science and Technology Information

Award: G941802975