2024/08/19 by Salomon Kabongo, Kabongo, Salomon, Jennifer D’Souza +1
Computer Science · Engineering · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Human Motion and Animation #Music Technology and Sound Studies #Speech and dialogue systems
paper · pdf · doi:10.48550/arxiv.2408.10141
openalex publication_date 2024/08/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study demonstrates the application of instruction finetuning of pretrained Large Language Models (LLMs) to automate the generation of AI research leaderboards, extracting (Task, Dataset, Metric, Score) quadruples from articles. It aims to streamline the dissemination of advancements in AI research by transitioning from traditional, manual community curation, or otherwise taxonomy-constrained natural language inference (NLI) models, to an automated, generative LLM-based approach. Utilizing the FLAN-T5 model, this research enhances LLMs' adaptability and reliability in information extraction, offering a novel method for structured knowledge representation.