From the 1 of 5 linked papers with an AI index.
5 papers
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
Quim Motger, Marc Oriol, Jordi Marco +1
The paper surveys research on multi-agent debate for large language model systems, introduces a three‑dimensional taxonomy of participants, interaction mechanisms, and agreement pr…
Evaluating LLM-Based Mobile App Recommendations: An Empirical Study
Quim Motger, Xavier Franch, Vincenzo Gervasi +1
Large Language Models (LLMs) are increasingly used to recommend mobile applications through natural language prompts, offering a flexible alternative to keyword-based app store sea…
Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Marc Oriol, Quim Motger, Jordi Marco +1
Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt e…
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Quim Motger, Marc Oriol, Max Tiessler +2
Opinion mining plays a vital role in analysing user feedback and extracting insights from textual data. While most research focuses on sentiment polarity (e.g., positive, negative,…
Leveraging Encoder-only Large Language Models for Mobile App Review Feature Extraction
Quim Motger, Alessio Miaschi, Felice Dell'Orletta +2
Mobile app review analysis presents unique challenges due to the low quality, subjective bias, and noisy content of user-generated documents. Extracting features from these reviews…