United States

We propose a CodeBERT-based system for detecting AI-generated code under severe cross-language and cross-domain distribution shift. Our approach conducts a controlled comparison between a fully frozen backbone and a partially fine-tuned configuration that unfreezes only the final transformer layer with discriminative learning rates. While partial fine-tuning substantially improves in-domain performance, the frozen backbone demonstrates stronger robustness under out-of-distribution evaluation. Our results highlight a trade-off between task adaptation and cross-language generalization in machine-generated code detection.

ACL 2026

Team_Omega at SemEval-2026 Task 13: Frozen vs. Trainable Representations for Out-of-Distribution AI-Generated Code Detection: A CodeBERT Fine-Tuning Study

out-of-distribution learning

ai-generated code detection

code classification

semeval

codebert

machine learning

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
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workshop paper

The Narrative Story Similarity and Narrative Representation Learning (NSNRL) task measures the narrative similarity between two stories based on three core aspects: the abstract theme, the course of action, and the outcomes. Our system leverages LLMs both for extracting high-level aspects and to encode them with state-of-the-art generalist embedding models. We then apply a series of embedding post-processing steps and learn to fit the embedding space with a Mahalanobis-like diagonal metric. We show that some of these techniques should not be applied universally, as they do not necessarily increase performance or overfit, depending on the base encoder. Our system outperforms the baseline only in Track B, ranking twelfth out of twenty-seven on the final leaderboard, while performing lower than the baseline accuracy in Track A.

Team UBSE at SemEval-2026 Task 4: Adapting Generalist Embeddings for Narrative Representations

This paper presents a neuro-symbolic ensemble for determining narrative similarity by moving beyond surface-level text matching toward structural and causal alignment. The architecture fuses three primary signals: action-focused neural embeddings that isolate event trajectories , a symbolic Structural Survival Ratio (SSR) that measures the preservation of discrete event tuples via dependency parsing , and high-level structural comparisons conducted by the gpt-5-mini model. Evaluated on the SemEval-2026 Task 4 test set, the integrated ensemble achieved an accuracy of 68.25%.

harapalb at SemEval-2026 Task 4: Multi-Signal Neuro-Symbolic Ensembles for Narrative Similarity

We describe the Seals-NLP system for SemEval-2026 Task 9 (POLAR) Subtask 1, binary polarization detection. Our study compares (i) fully fine-tuned encoder-only transformers, (ii) QLoRA-based fine-tuned open-weight LLMs, and (iii) zero-shot prompted LLMs. ModernBERT-large emerges as the most cost-effective option, matching or surpassing larger fine-tuned and zero-shot LLMs in macro-F1 while requiring substantially less memory and lower latency. An error analysis by failure mode and polarization subtype reveals systematic over-triggering on political cue words and under-detection of sarcastic vilification and multifaceted attacks in the POLAR dataset across all models.

Seals-NLP at SemEval-2026 Task 9: A Comparative Study of Transformer Architectures for Polarization Detection

This paper describes our submission to SemEval-2026 Task 8 on multi-turn retrieval-augmented generation (RAG). We propose a hybrid multi-stage pipeline that combines high-recall lexical retrieval, dual-embedding dense re-ranking with reciprocal rank fusion, LLM-based relevance judging, and strictly constrained evidence-grounded generation. Our design emphasizes robustness and faithfulness across the full retrieval-to-generation pipeline. Our results suggest that relevance-aware filtering and constrained generation are important for improving faithfulness and overall RAG performance.

GenAIus at SemEval-2026 Task 8: Beyond Retrieval with Relevance-Aware RAG for Faithful Multi-Turn Generation

We first use Qwen2.5-32B-Instruct model to generate hard negatives from three
narrative dimensions. We then train a Qwen3-Embedding-8B model with a multi-negative
contrastive objective and use self-distllation.

Team HITS at SemEval-2026 Task 4:Enhancing narrative text embedding model training with hard negatives generation and self-distillation

We describe our system for predicting sense plausibility in short narratives. Our approach centers on task decomposition: instead of predicting a score directly, we break the problem into simpler subtasks and combine their outputs. We further improve performance by ensembling complementary signals, including word sense disambiguation and fine-tuned embedding models. We also find empirical support for the one-homonym-per-translation principle of Hauer and Kondrak (2020a). Our best ensemble system achieves competitive performance in the official evaluation. Our code and data are available on GitHub.

UAlberta at SemEval-2026 Task 5: Disambiguating Stories via Task Decomposition

Our submission presented in this paper is for SemEval-2026 Task 9: Multilingual Text Classification Challenge - Polarization Detection and it covers all three subtasks: (1) binary polarization detection, (2) polarization type classification and (3) polarization manifestation identification. We adopt a systematic approach of research on short designed prompts by considering twelve designed prompts that are different in terminology clarity, detail of the definition, guidance of reasoning and in-context examples use. The experiments are conducted using aya-101 and Gemma3-27B, with the latter chosen for the submission at the end of the development through performance considerations. Our system has an average macro level \textbf{F1-score of 0.762 on Subtask 1, 0.587 on Subtask 2 and 0.444 on Subtask 3} with the average accuracy of 0.819, 0.678 and 0.498, respectively, on the official test set averaged among 22 languages, respectively. With cross-task and cross-lingual analysis, we demonstrate that prompt-based approaches can be used effectively to detect coarse-grained polarization but encounter more and more difficulties as far as fine-grained and multi-label sociolinguistic classification is concerned.

Lingo_Research_Group at SemEval-2026 Task 9: Evaluating Prompt Variants for Polarization Detection

We present a hybrid system for SemEval 2026
Task 5: Rating Plausibility of Word Senses in
Ambiguous Stories. Our approach reframes
LLMs as feature generators rather than direct
predictors. We combine two subsystems: one
that appends LLM-generated hints to the in-
put context and trains an encoder-based regres-
sion model, and another that feeds structured
hints from multiple LLM configurations into a
lightweight regression ensemble. We generate
multilingual enrichments to probe LLMs for
complementary signals and take advantage of
the fact that translation into certain languages
implicitly disambiguates word senses, making
the encoder more robust. The 50/50 ensem-
ble achieves 859/930 (92.37%) accuracy with
Spearman ρ= 0.8384 on the test set, exceed-
ing the estimated human ceiling of 89.2%. The
same LLM enrichments, processed through fun-
damentally different paradigms (tabular regres-
sion vs. full-text encoding), produce comple-
mentary errors that cancel under ensembling.
Notably, simple 50/50 averaging captures this
gain without any learned combiner, suggest-
ing that

Tifin India at SemEval-2026 Task 5: Semantic Bridge: Augmented Encoding for Word Sense Plausibility

Automatically determining whether source code is human written or produced by a specific family of large language models is becoming essential for reliable assessment, provenance tracking, and dataset curation. We present a lightweight yet competitive system for SemEval 2026 Task 13 Subtask B, which requires attributing each snippet to one of eleven classes: human or one of ten LLM families. Our method repurposes code oriented instruction tuned backbones from the Qwen2.5 Coder series as sequence classifiers and adapts them using QLoRA, combining frozen low precision weights with low rank trainable adapters to reduce memory and compute overhead. The core design choice addresses long snippets without losing evidence. Instead of truncating to a fixed context, we apply an overlapping sliding window strategy that expands long examples into multiple fixed length windows during training, all sharing the same label. For validation and test, windows are generated on the fly and their evidence is aggregated by averaging logits to yield a single prediction per snippet, enabling token complete use of the input while keeping inference stable. Our final submission ranked 8th on the official Subtask B test set leaderboard.

CodeDet-NITS at SemEval-2026 Task 13: AI Code Authorship Detection Beyond Truncation

This paper presents the NIT-Agartala-NLPTeam's submission to SemEval-2026 Task 9
on polarization detection in textual data. The
task comprises two subtasks: (i) binary classification to distinguish polarized from nonpolarized content, and (ii) multi-label classification to identify the specific type(s) of polarization. We propose a weighted soft-voting
ensemble framework that integrates multiple
fine-tuned large language models (LLMs). The
probabilistic outputs of the individual models are combined using weighted averaging
to effectively leverage their complementary
strengths and enhance overall performance.
Our system achieved a test macro F1-score of
78.6 (26th out of 44 teams) in Subtask 1 and
46.0 (18th out of 29 teams) in Subtask 2.

NIT-Agartala-NLP-Team at SemEval-2026 Task 9: A Weighted Soft-Voting Ensemble Framework of Fine-Tuned LLMs for Binary and Multi-Label Polarization Detection

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Team UBSE at SemEval-2026 Task 4: Adapting Generalist Embeddings for Narrative Representations

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Königsberg at SemEval-2026 Task 13: Beyond Language Models: A Low-Resource Feature-Driven and Data-Flow Embedding Approach for Machine-Generated Code Detection

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Team UBSE at SemEval-2026 Task 4: Adapting Generalist Embeddings for Narrative Representations

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Königsberg at SemEval-2026 Task 13: Beyond Language Models: A Low-Resource Feature-Driven and Data-Flow Embedding Approach for Machine-Generated Code Detection

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COMPANY

RESOURCES

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