Exploring Precision and Recall to assess the quality and diversity of LLMs
Florian Le Bronnec, Alexandre Verine, Benjamin Negrevergne, Yann Chevaleyre, Alexandre Allauzen
Abstract
We introduce a novel evaluation framework for Large Language Models (LLMs) such as Llama-2 and Mistral, focusing on importing Precision and Recall metrics from image generation to text generation. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora. By conducting a comprehensive evaluation of state-of-the-art language models, the study reveals new insights into their performance on open-ended generation tasks, which are not adequately captured by traditional benchmarks. The findings highlight a trade-off between the quality and diversity of generated samples, particularly when models are fine-tuned on instruction dataset or with human feedback. This work extends the toolkit for distribution-based NLP evaluation, offering insights into the practical capabilities and challenges that current LLMs face in generating diverse and high-quality text.- Anthology ID:
- 2024.acl-long.616
- Volume:
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 11418–11441
- Language:
- URL:
- https://aclanthology.org/2024.acl-long.616
- DOI:
- 10.18653/v1/2024.acl-long.616
- Cite (ACL):
- Florian Le Bronnec, Alexandre Verine, Benjamin Negrevergne, Yann Chevaleyre, and Alexandre Allauzen. 2024. Exploring Precision and Recall to assess the quality and diversity of LLMs. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11418–11441, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Exploring Precision and Recall to assess the quality and diversity of LLMs (Le Bronnec et al., ACL 2024)
- PDF:
- https://preview.aclanthology.org/autopr/2024.acl-long.616.pdf