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LLM finetuning system achieves 85th percentile in conspiracy detection task

By PulseAugur Editorial · [2 sources] ·

Researchers developed an mdok-style system for SemEval-2026 Task 10, which focuses on detecting conspiracy beliefs in Reddit comments. The system employed data augmentation and self-training techniques to fine-tune the Qwen3-32B model for this binary text-classification task. Achieving an 85th percentile ranking among 52 submissions, the approach demonstrated the adaptability of machine-generated text detection methods to conspiracy detection. AI

IMPACT Demonstrates a novel application of LLM fine-tuning for social media content analysis.

RANK_REASON The cluster contains an academic paper detailing a system for a specific NLP task.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM finetuning system achieves 85th percentile in conspiracy detection task

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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Dominik Macko ·

    mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection

    arXiv:2605.02712v1 Announce Type: new Abstract: SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system utilizes data augmentation and self-training (to co…

  2. arXiv cs.CL TIER_1 English(EN) · Dominik Macko ·

    mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection

    SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system utilizes data augmentation and self-training (to cope with a rather small amount of training data) …

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