This volume LNCS constitutes the proceedings of the 31st International Conference on Applications of Natural Language to Information Systems, NLDB 2026, held in Trondheim, Norway, June 17–19, 2026.
The 22 full papers presented in this volume were carefully reviewed and selected from 46 submissions. The proceedings contain Generative and Large Language Models; Social Media and Web Data; AI safety and ethics; Efficient/Low-resource methods in NLP; Information Retrieval and Text Mining; Explainable AI; Interpretability and Models Analysis in NLP.
Table of Contents:
.- 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.
