SmaLLEXT 2025
Small and Efficient Large Language Models for Knowledge Extraction 2025

Table of Contents

• Preface
  Summary: There were 7 papers submitted for peer review to this workshop. Out of these, 5 papers were accepted as peer-reviewed papers for this volume. 1-5
  

Peer-reviewed papers

• Exploring Approaches for Detecting Memorization of Recommender System Data in Large Language Models 6-14
  Antonio Colacicco , Vito Guida , Dario Di Palma , Fedelucio Narducci , Tommaso Di Noia
  
• Exploiting LLMs to Improve Recommendation Diversity 15-24
  Basar Yilmaz , Ali Eren Cankaya , Ismail Sengor Altingovde , Pinar Karagoz , Ismail Hakki Toroslu
  
• SEDA: A Self-Adapted Entity-Centric Data Augmentation for Boosting Grid-based Discontinuous NER Models 25-39
  Wen-Fang Su , Hsiao-Wei Chou , Wen-Yang Lin
  
• CliqueParcel: An Approach for Batching LLM Prompts that Jointly Optimizes Efficiency and Faithfulness 40-58
  Jiayi Liu , Tinghan Yang , Jennifer Neville
  
• Identifying Business Trends from Stock Market Data utilizing Domain-Specialized Sentence-RoBERTa and BERTopic Technique 59-64
  Ye Lim Jung , Hyoung Sun Yoo
  

Keynotes

• Automating Neural Network Design: Neural Architecture Search for Small and Efficient Models - Keynote 65-69
  Aaron Klein
  
• Machine Learning Without Data: Training Small Expert Agents With Minimal Examples - Keynote 70-77
  Jacek Golebiowski , Maciej Gryka , Vineeth Reddy Guda , Gabi Kadlecová , Bartek Kruszczyński , Selim Nowicki , Jonas Verschueren , Usman Zafar