迈向大语言模型中组合可泛化的语义解析：一项综述
Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey
AI总结：
本文综述了大语言模型在语义解析任务中组合泛化能力的局限性、改进方法和评估方案，为相关研究提供了起点。
摘要：
组合泛化是指模型仅通过观察基本元素，就能泛化到复杂的、未见过的实体组合类型的能力。这种泛化对于面向任务的对话、text-to-SQL 解析和信息检索等应用尤为重要，因为它们可能包含无限的复杂性。尽管大语言模型（LLMs）在广泛的 NLP 任务中取得了成功，但实现完美的组合泛化仍是少数尚未解决的最终前沿之一。过去几年中，人们对探索 LLMs 在语义解析任务中组合泛化能力的局限性、改进方法和评估指标的研究兴趣激增。在这项工作中，我们提出了一篇文献综述，旨在综合分析、方法和评估方案方面的最新进展，为该领域的从业者和研究人员提供一个起点。
英文摘要：
Compositional generalization is the ability of a model to generalize to complex, previously unseen types of combinations of entities from just having seen the primitives. This type of generalization is particularly relevant to the semantic parsing community for applications such as task-oriented dialogue, text-to-SQL parsing, and information retrieval, as they can harbor infinite complexity. Despite the success of large language models (LLMs) in a wide range of NLP tasks, unlocking perfect compositional generalization still remains one of the few last unsolved frontiers. The past few years has seen a surge of interest in works that explore the limitations of, methods to improve, and evaluation metrics for compositional generalization capabilities of LLMs for semantic parsing tasks. In this work, we present a literature survey geared at synthesizing recent advances in analysis, methods, and evaluation schemes to offer a starting point for both practitioners and researchers in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
