Related papers: Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)

Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)

URL: http://arxiv.org/abs/2301.13820v1
Date: Wed, 25 Jan 2023 16:12:43 GMT
Title: Explaining Large Language Model-Based Neural Semantic Parsers (Student Abstract)
Authors: Daking Rai (1), Yilun Zhou (2), Bailin Wang (2), Ziyu Yao (1) ((1) George Mason University, (2) Massachusetts Institute of Technology)
Abstract summary: Large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing. Our work studies different methods for explaining an LLM-based semantic semantic behaviors. We hope to inspire future research toward better understanding them.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: While large language models (LLMs) have demonstrated strong capability in structured prediction tasks such as semantic parsing, few amounts of research have explored the underlying mechanisms of their success. Our work studies different methods for explaining an LLM-based semantic parser and qualitatively discusses the explained model behaviors, hoping to inspire future research toward better understanding them.

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