United States

This paper presents the Georgetown University NLP (GUNLP) system developed for SemEval 2026 Task 10: Psycholinguistic Conspiracy Marker Extraction and Detection, addressing the classification of conspiratorial beliefs in Reddit posts (Subtask 2). Our approach leverages COVID-Twitter-BERT v2 (CT-BERT-v2) within a multi-task learning framework that jointly optimizes conspiracy classification and emotion label prediction through a dual-head architecture. To address data scarcity, we enrich the training set using paraphrasing-based data augmentation and GPT-5-generated chain-of-thought emotion annotations, effectively doubling the training corpus to approximately 8,600 examples. We evaluate two input configurations: text only and text concatenated with emotion labels. The emotion-aware configuration achieves the strongest performance with an F1 score of 0.87 on the official development set, outperforming the text-only baseline by five F1 points and demonstrating the value of paraphrased samples and affective auxiliary supervision for conspiracy detection in social media text.

ACL 2026

GUNLP at SemEval-2026 Task 10: Psycholinguistic Conspiracy Marker Extraction and Detection (PsyCoMark)

emotion-aware model

conspiracy detection

multi-task learning

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workshop paper

The rise of code-generating LLMs such as DeepSeek, Qwen, and Meta-LLaMA has improved developer productivity but also increased risks of plagiarism, copyright misuse, and insecure machine-generated code. While AI-text detection is well studied, machine-generated source-code detection especially across multiple languages, LLM families, and OOD conditions-remains underexplored. SemEval-2026 Task 13 addresses this via two subtasks: (A) binary human–machine code detection and (B) multi-class authorship attribution across ten LLM families. For Subtask A, we fine-tune RoBERTa, CodeBERT, GraphCodeBERT, and StarCoderBase-1B, introducing a stratified sampling strategy with class-weighted loss to mitigate imbalance and OOD shifts. For Subtask B, we mitigate the extreme human-class imbalance using undersampling, inverse-frequency weights, syntactic noising, and curriculum-based dual-path training with TinyStarCoderPy and CodeBERT. Both results show that long-context modeling, distribution-aware sampling, and noise-robust training are crucial for reliable in real-world settings. Overall, long-context modeling, distribution-aligned sampling, and lightweight noise-robust training emerge as key factors for reliable machine-generated code detection and authorship attribution.

Osint at SemEval-2026 Task 13: A Distribution-Aware Framework for Machine-Generated Code Detection and Multi-Source Authorship Attribution

This paper describes the system submitted by team Archaeology to SemEval-2026 Task~13 on AI-generated code detection. The shared task consists of three subtasks; we participate in Subtask-A (binary classification: human-written vs.\ AI-generated code) and Subtask-B (11-class attribution of the generating model).
Starting from a TF-IDF and Logistic Regression baseline, we fine-tune four pre-trained code models (CodeBERT, GraphCodeBERT, UniXcoder, and CodeT5+) with separate strategies for each subtask.
For Subtask-A, we use leave-one-language-out cross-validation, code augmentation, chunked inference with trimmed-mean aggregation, and threshold calibration on a difficult dataset.
For Subtask-B, we use sandwich token packing, class-balanced loss, and multi-seed ensembling with test-time augmentation. Our best submissions obtain macro-F1 scores of 0.737 on Subtask-A and 0.422 on Subtask-B.

Archaeology at SemEval-2026 Task 13: Fine-Tuning Pre-Trained Code Models for AI-Generated Code Detection

We describe our system for SemEval-2026 Task 13, Subtask B, which focuses on multi-class authorship attribution for code: given a code snippet, the goal is to predict whether it is human-written or generated by one of ten LLM families. The task presents two central challenges: severe class imbalance and long input sequences that frequently exceed the context length of encoder-based Transformers. To address these issues, we adopt a window-based fine-tuning and inference framework. During training, we randomly sample 512-token windows from each snippet and optimize a class-weighted cross-entropy objective with label smoothing. At inference time, we apply a sliding-window strategy and aggregate window-level logits to obtain a snippet-level prediction. We fine-tune three pretrained code encoders (CodeBERT, UniXcoder, and StarEncoder) under this framework and combine their outputs via majority voting. On the official validation split, our best single model (StarEncoder) achieves 0.60 macro F1. On the final test set, the three-model ensemble reaches 0.41 macro F1, ranking 10th on the leaderboard. Our results demonstrate that window-based modeling combined with imbalance-aware optimization provides a robust and reproducible baseline for multi-class LLM attribution under distribution shift.

ICI Innolabs at SemEval-2026 Task 13: Sliding Windows Meet Code Transformers

In this paper, we present the solution submitted by TUCNLP at SemEval-2026 Task~11: Disentangling Content and Formal Reasoning in Large Language Models. The task requires predicting the formal validity of categorical syllogisms while minimizing susceptibility to content-driven biases in English and 11 additional languages. We show that a modestly-sized model (Qwen3-8B) can achieve near-perfect logical reasoning on the English validity-only subtask, and large reductions in content effect on multilingual and premise-retrieval variants, when augmented with a multi-stage neuro-symbolic pipeline: LLM-based content stripping with iterative error correction converts natural language to abstract categorical forms, a classical symbolic parser validates against the twenty-four Aristotelian syllogistic forms, and asymmetric confidence thresholds mediate between symbolic and neural decisions. Across the four subtasks (ST1 to ST4), our system achieves accuracy ranging from 91.1\% to 100\% and bias-penalized ranking scores ($\mathcal{M}$) from 31.8 to 100.0, with the main bottleneck being overconfident neural predictions that bypass symbolic verification.

TUCNLP at SemEval-2026 Task 11: Neuro-Symbolic Content Stripping for Debiased Syllogistic Reasoning

We describe our system for SemEval-2026 Task 9 (POLAR), a shared task on detecting multilingual, multicultural, and multievent online polarization. POLAR includes three subtasks: polarization detection (ST1), polarization type classification (ST2), and polarization manifestation identification (ST3). We participated in ST1 and ST2 for Arabic. We compare three method families, encoder fine-tuning, zero-shot prompting, and retrieval-augmented in-context learning (RAG-ICL), across six LLMs and six Arabic encoder models. For ST1, RAG-ICL with Gemma-3-27B reaches F1\textsubscript{macro}~=~0.83 on the test set, a $+0.33$ absolute gain over zero-shot prompting. For ST2, a pipeline that uses the best ST1 encoder as a hard filter before RAG-ICL classification reduces hallucination from 58\% to 19\% and yields F1\textsubscript{macro}~=~0.62. English prompts consistently outperform Arabic prompts, and advanced prompting strategies (chain-of-thought, self-refinement) do not improve over standard RAG-ICL.

NAMAA at SemEval-2026 Task 9: Comparing Generative, Retrieval-Augmented, and Discriminative Methods for Arabic Online Polarization Detection and Type Classification

We describe our system for SemEval-2026 Task 13, Subtask B: code classification into 11 categories (human-written or generated by one of 10 LLM families). The task presents extreme class imbalance and distribution shift across multiple generators provided in the dataset (31 in training, 59 in test, with 36 unseen). On that focus, we approached with two components: (1) UniXcoder as the encoder with Label-Distribution-Aware Margin (LDAM) loss for handling class imbalance, which provides a +7% absolute improvement over the cross-entropy baseline; and (2) a diverse ensemble of 12 models trained with different objectives and architectures which is detailed in the appendix, combined with hard voting. Our system achieves 41.28% Macro F1 on the official test set. We find that loss engineering and ensemble diversity matter more than domain adaptation techniques, which consistently degraded test performance.

Codexa at SemEval-2026 Task 13: Loss Engineering and Diverse Ensemble Strategies for Multi-Class Code Authorship Attribution

We present the results and the main findings of SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios. Our task featured three subtasks. Subtask A is a binary classification task
that determines whether a given code snippet is written by a human or generated by a machine. This subtask focuses on the development of robust methods for AI-generated code identification, since the training and the test data splits have code in different languages and cover diverse usage domains. Subtask B focuses on defining synthetic code smells and requires participants to identify the provenance of the generator family of the model that generated the given code snippet. Subtask C aims at more fine-grained attribution of the written code: whether it was fully AI-generated, fully human-written, produced in human-AI collaboration (hybrid) or by a model tuned or prompted to give human-like code. The task attracted a large number of team members: subtask A (81), subtask B (34), and subtask C (32). In this study, we present the task, analyze the results and discuss the submissions of the system and the methods they used.

SemEval-2026 Task 13: Detecting Machine-Generated Code with Multiple Programming Languages, Generators, and Application Scenarios

We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labeled with the presence of polarization, polarization type, and polarization manifestation. Participants were asked to predict labels in three subtasks: (1) detecting the presence of polarization, (2) identifying the type of polarization, and (3) recognizing the polarization manifestation. The three tasks attracted over 1,000 participants worldwide and more than 10k submissions on Codabench. We received final submissions from 67 teams and 69 system description papers. We report the baseline results and analyze the performance of the best-performing systems, highlighting the most common approaches and the most effective methods across different subtasks and languages. The dataset and other resources for this task are publicly available.

SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization

This paper presents our system developed for the SemEval-2026 Task 10: PsyCoMark Psycholinguistic Conspiracy Marker Extraction and Detection. on Subtask 1: Conspiracy Marker Extraction. on Subtask 2: Conspiracy Detection. To this end, we focus on English language use four different pre-trained languages models: models–distilbert–distilbert-base uncased, models–distilbert–distilbert-base-multilingual-cased, models–lxyuan–distilbert-base-multilingual-cased-sentiments-student, and models–microsoft–deberta-v3-base. We experiment with 1) the training set data is analyzed visually, 2) use the gemma-3-27b-it generative model to perform data augmentation on the training dataset through prompts for Subtask 2: Conspiracy Detection, and 3) multiple numbers of single models are trained on the training set data. We further study the influence of different hyperparameters on the single model and select the best single model for the prediction of the test set. Our submission achieved the good ranking place in the test set leaderboard. For Subtask 1, the evaluation criteria for this task mainly consist of the aggregate results of the four markers: Actor, Action, Effect, and Victim, and they are measured using the Macro F1 score. For Subtask 2, this task is essentially a binary classification task for text. Performance will be evaluated using macro-averaged F1 score. In other words, this subtask evaluated using Weighted F1 score across different sentences and cultural contexts. For Subtask 1 and Subtask 2, our best approach is to obtain the results are Macro F1 score 0.1587 and Weighted F1 score 0.7411 separately. For the final ranking, organizers will use the aggregate results of Macro F1 score and Weighted F1 score. Even so, our approach has yielded good results.

wangkongqiang at SemEval-2026 Task 10: PsyCoMark- Psycholinguistic Conspiracy Marker Extraction and Detection

This paper describes the system developed by team for SemEval-2026 Task 2, Subtask 1: Longitudinal Affect Assessment. Our goal is to predict Valence and Arousal from ecological essays and feeling words over time. We propose an efficient hybrid framework that uses quantized 7B-scale language models as deterministic meta-feature extractors and combines them with an ensemble of DeBERTa, RoBERTa, and DistilBERT encoders. The system is designed to run on a single consumer-grade RTX 5060 Ti (16GB) GPU while remaining competitive. To bridge discrete supervision and continuous evaluation, we train the model as an ordinal classification problem and decode class probabilities into continuous scores through expected-value decoding. Our best system achieved an overall V&A average of 0.587, with per-dimension composite correlations of 0.647 for Valence and 0.527 for Arousal, ranking 3rd out of 31 teams. The results show that lightweight SLM-derived priors and multi-encoder fusion provide a strong performance--efficiency trade-off, especially for Arousal, where contextual anchoring is crucial.

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