Improving Diversity in Language Models: When Temperature Fails, Change the Loss
Alexandre Verine, Florian Le Bronnec, Kunhao Zheng, Alexandre Allauzen, Yann Chevaleyre, benjamin negrevergne
Abstract
Increasing diversity in language models is a challenging yet essential objective. A common approach is to raise the decoding temperature. In this work, we investigate this approach through a simplistic yet common case to provide insights into why decreasing temperature can improve quality (Precision), while increasing it often fails to boost coverage (Recall). Our analysis reveals that for a model to be effectively tunable through temperature adjustments, it must be trained toward coverage. To address this, we propose rethinking loss functions in language models by leveraging the Precision-Recall framework. Our results demonstrate that this approach achieves a substantially better trade-off between Precision and Recall than merely combining negative log-likelihood training with temperature scaling. These findings offer a pathway toward more versatile and robust language modeling techniques.
BibTeX
@inproceedings{
verine2025improving,
title={Improving Diversity in Language Models: When Temperature Fails, Change the Loss},
author={Alexandre Verine and Florian Le Bronnec and Kunhao Zheng and Alexandre Allauzen and Yann Chevaleyre and benjamin negrevergne},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=RsyMfsqzeG}
}