Reliable, Reproducible, and Really Fast Leaderboards with Evalica
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912158309154816 |
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| author | Ustalov, Dmitry |
| author_facet | Ustalov, Dmitry |
| contents | The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible model leaderboards. This paper presents its design, evaluates its performance, and demonstrates its usability through its Web interface, command-line interface, and Python API. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11314 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reliable, Reproducible, and Really Fast Leaderboards with Evalica Ustalov, Dmitry Computation and Language 62-04 D.2.3 The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible model leaderboards. This paper presents its design, evaluates its performance, and demonstrates its usability through its Web interface, command-line interface, and Python API. |
| title | Reliable, Reproducible, and Really Fast Leaderboards with Evalica |
| topic | Computation and Language 62-04 D.2.3 |
| url | https://arxiv.org/abs/2412.11314 |