While reaching for NLP systems that maximize accuracy, other important
metrics of system performance are often overlooked. Prior models are easily
forgotten despite their possible suitability in settings where large computing
resources are unavailable or relatively more costly. In this paper, we perform
a broad comparative evaluation of document-level sentiment analysis models with
a focus on resource costs that are important for the feasibility of model
deployment and general climate consciousness. Our experiments consider
different feature extraction techniques, the effect of ensembling,
task-specific deep learning modeling, and domain-independent large language
models (LLMs). We find that while a fine-tuned LLM achieves the best accuracy,
some alternate configurations provide huge (up to 24, 283 *) resource savings
for a marginal (<1%) loss in accuracy. Furthermore, we find that for smaller
datasets, the differences in accuracy shrink while the difference in resource
consumption grows further.

该论文对文档级情感分析模型进行了广泛比较评估，在关注模型部署可行性和资源消耗的重要指标方面，考虑了不同的特征提取技术、集成效果、任务特定的深度学习建模和领域无关的大型语言模型（LLMs）。研究发现，尽管微调的 LLM 获得了最高的准确率，但某些替代配置在资源消耗方面提供了巨大的节省，而准确率只有微小的损失。此外，研究还发现，在较小的数据集上，准确率的差异越来越小，而资源消耗的差异却越来越大。