@inproceedings{lukasik-etal-2024-regression,
    title = "Regression Aware Inference with {LLM}s",
    author = "Lukasik, Michal  and
      Narasimhan, Harikrishna  and
      Menon, Aditya Krishna  and
      Yu, Felix  and
      Kumar, Sanjiv",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-emnlp.799/",
    doi = "10.18653/v1/2024.findings-emnlp.799",
    pages = "13667--13678",
    abstract = "Large language models (LLMs) have shown strong results on a range of applications, including regression and scoring tasks.Typically, one obtains outputs from an LLM via autoregressive sampling from the model`s output distribution. We show that this inference strategy can be sub-optimal for common regression and scoring evaluation metrics. As a remedy, we build on prior work on Minimum Bayes Risk decoding,and propose alternate inference strategies that estimate the Bayes-optimal solution for regression and scoring metrics in closed-form from sampled responses.We show that our proposal significantly improves over baselines across datasets and models."
}