Registration

14:00-15:20

Opening session

• Opening ceremony
• Keynote Speaker 1

Coffee break

15:50-16:50

Session 1: Generative Models and LLMs I

• Summarising Regulations: an Empirical Study of Long-Document Summarisation Methods under Extreme Compression

• ThinknCheck: Grounded Claim Verification with Compact, Reasoning-Driven, and Interpretable Models

09:00-10:00

Session 2: Social Media and Web Data

• Ontology-Augmented Prompt Engineering for Aspect-Based Sentiment Classification

• Consp2VecD: A Dataset based on Emotional Dynamics expressed by Reddit Conspiracy Groups for Information Disorder Analysis

• Adaptive Filtering for Large Language Models

Session 3: AI Safety and Ethics

• BEADS: Bias Evaluation Across Domains

• Mitigating Gender Bias in English to Romanian Machine Translation

• Zoom In Disparities in Healthcare LLM Q&A

11:00-11:30

Coffee Break

Keynote Speaker 2

12:30-14:00

Lunch

14:00-15:00

Session 4: Generative Models and LLMs II

• What Do Claim Verification Datasets Actually Test? A Reasoning Trace Analysis

• Using Text Simplification in Norwegian News Summarization

• Temporal Reframing as a Historical Reasoning Task for Large Language Models

Session 5: Efficient/Low-resource Methods in NLP

• Tokenizations for Austronesian Language Models: study on languages in Indonesia Archipelago

• Efficient Error-Type Transfer for Grammatical Error Detection via Embedding Alignment

• Towards Robust Uzbek Neural Dependency Parsing: Cross-Treebank Training

Coffee Break

Session 6: Information Retrieval and Text Mining

• Adapting GPT for Egyptian Arabic–English Code-Switched Sentiment Analysis through Prompting, Retrieval, and Sentiment-Guided Fine-Tuning

• Evaluating Noisy Optimization in Finetuning LMs for Neural Ranking

• Evaluating LLM-Generated Wikipedia Content: Political Topics in a French Setting

09:30-10:30

Session 7: Explainable AI

• When Words Move Markets: Interpretable Behavioural and Robustness Analysis of LLMs for Financial Sentiment Reasoning via Local Perturbation Explanations

• Attention-Pruned SHAP: Accelerating SHAP-Based Explainability with Attention-Guided Feature Pruning

• Temporal Structure in LLM Reasoning:Analyzing Hidden States and Semantic Embeddings

Coffee break

11:00-11:40

Session 8: Interpretability and Model Analysis in NLP

• Expansion Is Not Enough: Revisiting Dynamically Expandable Networks for NLP Tasks

• Automated ICD-10 Coding Approaches with UMLS Integration and Model Interpretability Support

Closing Ceremony