[2408.10141] Instruction Finetuning for Leaderboard Generation from Empirical AI Research
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Computer Science > Computation and Language

arXiv:2408.10141 (cs)
[Submitted on 19 Aug 2024]

Title:Instruction Finetuning for Leaderboard Generation from Empirical AI Research

Authors:Salomon Kabongo, Jennifer D'Souza
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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.
Comments: arXiv admin note: text overlap with arXiv:2407.02409
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2408.10141 [cs.CL]
  (or arXiv:2408.10141v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2408.10141

Submission history

From: Salomon Kabongo Kabenamualu [view email]
[v1] Mon, 19 Aug 2024 16:41:07 UTC (46 KB)
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