Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey
CoRR(2024)
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.
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