As the size of the pre-trained language model (PLM) continues to increase,
numerous parameter-efficient transfer learning methods have been proposed
recently to compensate for the tremendous cost of fine-tuning. Despite the
impressive results achieved by large pre-trained language models (PLMs) and
various parameter-efficient transfer learning (PETL) methods on sundry
benchmarks, it remains unclear if they can handle inputs that have been
distributionally shifted effectively. In this study, we systematically explore
how the ability to detect out-of-distribution (OOD) changes as the size of the
PLM grows or the transfer methods are altered. Specifically, we evaluated
various PETL techniques, including fine-tuning, Adapter, LoRA, and
prefix-tuning, on three different intention classification tasks, each
utilizing various language models with different scales.

本研究系统地探讨了在预训练语言模型（Pre-trained Language Models）规模扩大或转移方法改变时，检测样本分布变化的能力如何随之改变，着重评估了各种 PETL 技术在三个不同意图分类任务上的效果。

使用参数高效迁移学习探索语言模型的越界鲁棒性

Probing Out-of-Distribution Robustness of Language Models with Parameter-Efficient Transfer Learning

Neural models have achieved significant results on the text-to-SQL task, in
which most current work assumes all the input questions are legal and generates
a SQL query for any input. However, in the real scenario, users can input any
text that may not be able to be answered by a SQL query. In this work, we
propose TriageSQL, the first cross-domain text-to-SQL question intention
classification benchmark that requires models to distinguish four types of
unanswerable questions from answerable questions. The baseline RoBERTa model
achieves a 60% F1 score on the test set, demonstrating the need for further
improvement on this task. Our dataset is available at
this https URL

本研究提出 TriageSQL，旨在解决跨领域的文本到 SQL 题意分类问题，包括将无法回答的问题和可回答问题区分开来。RoBERTa 模型在测试集上取得 60% 的 F1 分数，表明需要在这个任务上进一步改进。