
This repository contains benchmark datasets (images and text), prompts, ground truths, and evaluation scripts for assessing the performance of large language models (LLMs) on humanities-related tasks. The suite is designed as a resource for researchers and practitioners interested in systematically evaluating how well various LLMs perform on digital humanities (DH) tasks involving visual and text-like materials. For detailed test results and model comparisons, visit our results dashboard at https://rise-services.rise.unibas.ch/benchmarks/.
If you use this software, please cite it using the metadata from this file.
LLM, benchmark, digital humanities
LLM, benchmark, digital humanities
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
