From the 1 of 9 linked papers with an AI index.
9 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…
Characterizing Datasets for LLM-based Requirements Engineering: A Systematic Mapping Study
Quim Motger, Carlota Catot, Xavier Franch
Large Language Models (LLMs) depend on high-quality, domain-specific natural language datasets. This dependency is particularly pronounced in Requirements Engineering (RE), where c…
Towards a Software Reference Architecture for Natural Language Processing Tools in Requirements Engineering
Julian Frattini, Quim Motger
Natural Language Processing (NLP) tools support requirements engineering (RE) tasks like requirements elicitation, classification, and validation. However, they are often developed…
FeClustRE: Hierarchical Clustering and Semantic Tagging of App Features from User Reviews
Max Tiessler, Quim Motger
[Context and motivation.] Extracting features from mobile app reviews is increasingly important for multiple requirements engineering (RE) tasks. However, existing methods struggle…
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…