Instruction Finetuning for Leaderboard Generation from Empirical AI Research

TL;DR

This paper presents an instruction finetuning approach for Large Language Models to automatically generate AI research leaderboards by extracting structured information from articles, improving efficiency over manual curation.

Contribution

It introduces a novel method using instruction finetuning of LLMs, specifically FLAN-T5, for automated extraction of structured research data from scientific articles.

Findings

Successful extraction of (Task, Dataset, Metric, Score) quadruples from articles.

Enhanced adaptability and reliability of LLMs in knowledge extraction.

Automated leaderboard generation reduces manual effort and speeds up dissemination.

Abstract

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.