论文标题
估计和改善语言模型鲁棒性的方法
Methods for Estimating and Improving Robustness of Language Models
论文作者
论文摘要
尽管表现出色，但大型语言模型（LLMS）还是臭名昭著的缺陷，因为它们偏爱简单的，表面级的文本关系而不是问题的完全语义复杂性。该提案调查了该问题的共同点，其在训练领域之外概括的能力较弱。我们调查了多种研究方向，提供了模型泛化能力的估计，发现将其中一些措施纳入培训目标会导致神经模型的分布鲁棒性增强。基于这些发现，我们提出了未来的研究指导，以增强LLM的鲁棒性。
Despite their outstanding performance, large language models (LLMs) suffer notorious flaws related to their preference for simple, surface-level textual relations over full semantic complexity of the problem. This proposal investigates a common denominator of this problem in their weak ability to generalise outside of the training domain. We survey diverse research directions providing estimations of model generalisation ability and find that incorporating some of these measures in the training objectives leads to enhanced distributional robustness of neural models. Based on these findings, we present future research directions towards enhancing the robustness of LLMs.
