shared-task

This paper describes the use of NLP methods including LSTM, BERT, and LLMs for analyzing mental health states from social media posts as part of the CLPsych 2026 shared task, achieving top consistency scores for summarization.

This paper presents an LLM-based pipeline for analyzing mental health changes from sequentially ordered social media posts, participating in the CLPsych 2026 shared task. It performs post-level assessment and user-level temporal modeling to capture shifts in psychological well-being.

This paper presents DistilledGemma, a system for person-place relation extraction from multilingual historical newspaper articles using a three-stage knowledge distillation pipeline from a 26B Gemma teacher to a 2.3B student, achieving competitive accuracy and efficiency in the HIPE-2026 shared task.

This paper describes UOL@IDEM's closed-track submission to the BEA 2026 shared task on L1-aware vocabulary difficulty prediction, combining multilingual contextual representations with engineered features. The system achieves competitive RMSE scores for Spanish, German, and Chinese, with frequency being the most stable predictor.

This paper presents an overview of the QIAS 2026 shared task on Islamic inheritance reasoning, evaluating LLMs on multi-step legal and numerical reasoning using the MAWARITH benchmark.

CUNY's submission to the CLPsych 2026 shared task uses a pipeline approach combining in-context learning with open-weight LLMs, supervised classifiers, and retrieval-augmented generation to classify and summarize mental health changes from Reddit timelines, achieving top rankings on multiple subtasks.

This paper presents findings from the Counter Turing Test shared task on AI-generated text detection, with top systems achieving perfect binary classification but significantly lower performance in model attribution, highlighting the difficulty of distinguishing outputs from different large language models.

University of Florida Gators submission to the AmericasNLP 2026 shared task on cultural image captioning for Indigenous languages, using a two-stage pipeline with Qwen2.5-VL for Spanish captioning and retrieval-augmented Gemini 2.5 Flash for target-language translation, achieving significant improvements over the baseline.

This paper presents a two-stage adaptation method for LLM-based multilingual coreference resolution, achieving first place in the LLM track of CRAC 2026 with a CoNLL F1 of 74.32. The approach fine-tunes Gemma-3-27b using a multilingual base adapter followed by dataset-specific adapters.

This paper describes two models for vocabulary difficulty prediction: a black-box LLM fine-tuned with a soft-target loss achieving high accuracy, and an explainable model providing insights into difficulty factors. The models were part of the BEA 2026 Shared Task and achieve strong correlations.

This paper details the RETUYT-INCO team's participation in the BEA 2026 Shared Task 2, introducing a meta-prompting approach for rubric-based scoring of German short answers.

This paper presents a system for the EEUCA 2026 shared task on toxicity detection in gaming chat, achieving 4th place by fine-tuning Llama 3.1 8B with synthetic data augmentation. It highlights a 'validation trap' phenomenon where high validation scores do not correlate with test performance due to dataset distribution shifts.