Summary of Exploring the Latest Llms For Leaderboard Extraction, by Salomon Kabongo et al.
Exploring the Latest LLMs for Leaderboard Extraction
by Salomon Kabongo, Jennifer D’Souza, Sören Auer
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the effectiveness of four Large Language Models (LLMs) in extracting information from empirical AI research articles. Specifically, it investigates how different LLMs – Mistral 7B, Llama-2, GPT-4-Turbo, and GPT-4.o – perform when given three types of contextual inputs: DocTAET, DocREC, and DocFULL. The study evaluates the models’ ability to generate quadruples containing task, dataset, metric, and score information from research papers. The results provide valuable insights into the strengths and limitations of each model and context type, offering guidance for future AI research automation efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well four big language models can understand scientific articles about artificial intelligence. It wants to see which one is best at pulling out important information from these papers. The researchers tested these models with different amounts of context – just the title and summary, or more details like results and conclusions. They found that some models are better than others at doing this task, and they think their results will help scientists in the future. |
Keywords
» Artificial intelligence » Gpt » Llama
