Summary of Assessing the Effectiveness Of Gpt-4o in Climate Change Evidence Synthesis and Systematic Assessments: Preliminary Insights, by Elphin Tom Joe and Sai Dileep Koneru and Christine J Kirchhoff
Assessing the Effectiveness of GPT-4o in Climate Change Evidence Synthesis and Systematic Assessments: Preliminary Insights
by Elphin Tom Joe, Sai Dileep Koneru, Christine J Kirchhoff
First submitted to arxiv on: 2 Jul 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 research paper investigates the potential of using GPT-4o, a large language model, to aid in evidence synthesis and systematic assessment tasks. The study aims to leverage the capabilities of LLMs to supplement traditional workflows involving manual literature reviews by domain experts. Specifically, the authors assess the efficacy of GPT-4o on a sample dataset from the Global Adaptation Mapping Initiative (GAMI), focusing on climate change adaptation-related feature extraction across three levels of expertise. The results show that while GPT-4o achieves high accuracy in low-expertise tasks like geographic location identification, its performance is less reliable in intermediate and high-expertise tasks such as stakeholder identification and assessment of depth of the adaptation response. The study highlights the need for designing assessment workflows that utilize the strengths of models like GPT-4o while providing refinements to improve their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses a super smart computer program called GPT-4o to help with tasks that require looking at lots of scientific papers and summarizing what’s important. Right now, humans do this work, but it takes a long time and requires many people. The researchers want to see if they can use GPT-4o to help make the process faster and more efficient. They tested the program on some climate change-related papers and found that it does a great job at identifying basic information, but struggles with more complex tasks like figuring out who is involved or what kinds of actions are being taken. This shows that we need to find ways to improve these programs so they can be really helpful. |
Keywords
» Artificial intelligence » Feature extraction » Gpt » Large language model
