Summary of Cutting Through the Clutter: the Potential Of Llms For Efficient Filtration in Systematic Literature Reviews, by Lucas Joos et al.
Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews
by Lucas Joos, Daniel A. Keim, Maximilian T. Fischer
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Digital Libraries (cs.DL); Human-Computer Interaction (cs.HC)
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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 addresses the challenge of creating systematic literature reviews, which are essential in academia but labor-intensive to produce. Conventional keyword-based filtering techniques can be inadequate due to semantic ambiguities and inconsistent terminology, leading to sub-optimal outcomes. To improve efficiency, speed, and precision, this study evaluates the potential of Large Language Models (LLMs) in enhancing literature review filtering. By using models as classification agents on a structured database, common problems like hallucinations are prevented. The authors test the real-world performance of LLMs like GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Flash, and Llama3 with simple prompting on a dataset of over 8.3k potentially relevant articles. Results show that LLMs can significantly reduce manual research time from weeks to minutes while achieving recalls >98.8% at or beyond the typical human error threshold. This study not only improves literature review methodology but also sets the stage for further AI integration in academic research practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to find and select relevant articles for a big project called a systematic literature review. Right now, this process is very time-consuming and prone to mistakes. The researchers ask if using special language models can help make the process faster, better, and more accurate. They test these models on a huge database of over 8,300 articles and find that they can significantly reduce the time it takes to do the research while still producing high-quality results. This study shows how AI can be used to make academic research easier and more efficient. |
Keywords
» Artificial intelligence » Classification » Claude » Gemini » Gpt » Precision » Prompting
