All Stories

  1. AIDME: A Scalable, Interpretable Framework for AI-Aided Scoping Reviews
  2. Impersonating the Crowd: Evaluating LLMs' Ability to Replicate Human Judgment in Misinformation Assessment
  3. Preaching to the ChoIR: Lessons IR Should Share with AI
  4. PILs of Knowledge: A Synthetic Benchmark for Evaluating Question Answering Systems in Healthcare
  5. The Magnitude of Truth: On Using Magnitude Estimation for Truthfulness Assessment
  6. Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking
  7. Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas
  8. Report on the 14th Italian Information Retrieval Workshop (IIR 2024)
  9. The Elusiveness of Detecting Political Bias in Language Models: The Impact of Question Wording
  10. Generative AI for Energy: Multi-Horizon Power Consumption Forecasting using Large Language Models
  11. Understanding the Barriers to Running Longitudinal Studies on Crowdsourcing Platforms
  12. Combining Large Language Models and Crowdsourcing for Hybrid Human-AI Misinformation Detection
  13. Data Bias Management
  14. How Many Crowd Workers Do I Need? On Statistical Power When Crowdsourcing Relevance Judgments
  15. Combining Human and Machine Confidence in Truthfulness Assessment
  16. Preferences on a Budget: Prioritizing Document Pairs when Crowdsourcing Relevance Judgments
  17. Crowd_Frame: A Simple and Complete Framework to Deploy Complex Crowdsourcing Tasks Off-the-Shelf