SubData: A Python Library to Collect and Combine Datasets for Evaluating LLM Alignment on Downstream Tasks
Guardado en:
| 發表在: | arXiv.org (Dec 21, 2024), p. n/a |
|---|---|
| 主要作者: | |
| 其他作者: | , |
| 出版: |
Cornell University Library, arXiv.org
|
| 主題: | |
| 在線閱讀: | Citation/Abstract Full text outside of ProQuest |
| 標簽: |
沒有標簽, 成為第一個標記此記錄!
|
| Resumen: | With the release of ever more capable large language models (LLMs), researchers in NLP and related disciplines have started to explore the usability of LLMs for a wide variety of different annotation tasks. Very recently, a lot of this attention has shifted to tasks that are subjective in nature. Given that the latest generations of LLMs have digested and encoded extensive knowledge about different human subpopulations and individuals, the hope is that these models can be trained, tuned or prompted to align with a wide range of different human perspectives. While researchers already evaluate the success of this alignment via surveys and tests, there is a lack of resources to evaluate the alignment on what oftentimes matters the most in NLP; the actual downstream tasks. To fill this gap we present SubData, a Python library that offers researchers working on topics related to subjectivity in annotation tasks a convenient way of collecting, combining and using a range of suitable datasets. |
|---|---|
| ISSN: | 2331-8422 |
| Fuente: | Engineering Database |