Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912430676770816 |
|---|---|
| author | Joos, Lucas Keim, Daniel A. Fischer, Maximilian T. |
| author_facet | Joos, Lucas Keim, Daniel A. Fischer, Maximilian T. |
| contents | Systematic literature reviews (SLRs) are essential but labor-intensive due to high publication volumes and inefficient keyword-based filtering. To streamline this process, we evaluate Large Language Models (LLMs) for enhancing efficiency and accuracy in corpus filtration while minimizing manual effort. Our open-source tool LLMSurver presents a visual interface to utilize LLMs for literature filtration, evaluate the results, and refine queries in an interactive way. We assess the real-world performance of our approach in filtering over 8.3k articles during a recent survey construction, comparing results with human efforts. The findings show that recent LLM models can reduce filtering time from weeks to minutes. A consensus scheme ensures recall rates >98.8%, surpassing typical human error thresholds and improving selection accuracy. This work advances literature review methodologies and highlights the potential of responsible human-AI collaboration in academic research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews Joos, Lucas Keim, Daniel A. Fischer, Maximilian T. Machine Learning Digital Libraries Human-Computer Interaction H.5.2 Systematic literature reviews (SLRs) are essential but labor-intensive due to high publication volumes and inefficient keyword-based filtering. To streamline this process, we evaluate Large Language Models (LLMs) for enhancing efficiency and accuracy in corpus filtration while minimizing manual effort. Our open-source tool LLMSurver presents a visual interface to utilize LLMs for literature filtration, evaluate the results, and refine queries in an interactive way. We assess the real-world performance of our approach in filtering over 8.3k articles during a recent survey construction, comparing results with human efforts. The findings show that recent LLM models can reduce filtering time from weeks to minutes. A consensus scheme ensures recall rates >98.8%, surpassing typical human error thresholds and improving selection accuracy. This work advances literature review methodologies and highlights the potential of responsible human-AI collaboration in academic research. |
| title | Cutting Through the Clutter: The Potential of LLMs for Efficient Filtration in Systematic Literature Reviews |
| topic | Machine Learning Digital Libraries Human-Computer Interaction H.5.2 |
| url | https://arxiv.org/abs/2407.10652 |