In which fields do ChatGPT scores align better than citations with research quality?
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914312807776256 |
|---|---|
| author | Thelwall, Mike |
| author_facet | Thelwall, Mike |
| contents | Although citation-based indicators are widely used for research evaluation, they are not useful for recently published research, reflect only one of the three common dimensions of research quality, and have little value in some social sciences, arts and humanities. Large Language Models (LLMs) have been shown to address some of these weaknesses, with ChatGPT-4o mini showing the most promising results, although on incomplete data. This article reports by far the largest scale evaluation of ChatGPT-4o mini yet and also evaluates its larger sibling ChatGPT-4o and ChatGPT-5 mini. Based on comparisons between LLM scores, averaged over 5 repetitions, and departmental average quality scores for 107,212 UK-based refereed journal articles, ChatGPT-4o is marginally better than ChatGPT-4o mini in most of the 34 field-based Units of Assessment (UoAs) tested, although combining both gives better results than either one. ChatGPT-4o scores have a positive correlation with research quality in 33 of the 34 UoAs, with the results being statistically significant in 31. The most substantial exception is Physics, for which citations are more useful. ChatGPT-4o scores had a higher correlation with research quality than long term citation rates in 21 out of 34 UoAs and a higher correlation than short term citation rates in 26 out of 34 UoAs. ChatGPT-5 mini has even stronger correlations overall. In summary, the results give the first large scale evidence that ChatGPT-4o and ChatGPT-5 mini are competitive with citations as new research quality indicator sources. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_04464 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | In which fields do ChatGPT scores align better than citations with research quality? Thelwall, Mike Digital Libraries Although citation-based indicators are widely used for research evaluation, they are not useful for recently published research, reflect only one of the three common dimensions of research quality, and have little value in some social sciences, arts and humanities. Large Language Models (LLMs) have been shown to address some of these weaknesses, with ChatGPT-4o mini showing the most promising results, although on incomplete data. This article reports by far the largest scale evaluation of ChatGPT-4o mini yet and also evaluates its larger sibling ChatGPT-4o and ChatGPT-5 mini. Based on comparisons between LLM scores, averaged over 5 repetitions, and departmental average quality scores for 107,212 UK-based refereed journal articles, ChatGPT-4o is marginally better than ChatGPT-4o mini in most of the 34 field-based Units of Assessment (UoAs) tested, although combining both gives better results than either one. ChatGPT-4o scores have a positive correlation with research quality in 33 of the 34 UoAs, with the results being statistically significant in 31. The most substantial exception is Physics, for which citations are more useful. ChatGPT-4o scores had a higher correlation with research quality than long term citation rates in 21 out of 34 UoAs and a higher correlation than short term citation rates in 26 out of 34 UoAs. ChatGPT-5 mini has even stronger correlations overall. In summary, the results give the first large scale evidence that ChatGPT-4o and ChatGPT-5 mini are competitive with citations as new research quality indicator sources. |
| title | In which fields do ChatGPT scores align better than citations with research quality? |
| topic | Digital Libraries |
| url | https://arxiv.org/abs/2504.04464 |