.- Generative and Large Language Models.
.- Summarising Regulations: an Empirical Study of Long-Document Summarisation Methods
under Extreme Compression.
.- ThinknCheck: Grounded Claim Verif ication with Compact, Reasoning-Driven, and
Interpretable Models.
.- Secure Coding Unleashed: Boosting Productivity With On-Premise LLM-Powered IDE Plugins.
.- 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 Model.
.- 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 Model.
.- AI safety and ethics.
.- Bias Evaluation Across Domains.
.- Mitigating Gender Bias in English to Romanian Machine Translation.
.- Zoom In Disparities in Healthcare LLM Q&A.
.- 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.
.- 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.
.- 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.
.- Interpretability and Models 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.
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