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

arXiv: https://arxiv.org/abs/2301.13820 · HTML: https://mlsanity.com/paper/2301.13820/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 7 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models](https://mlsanity.com/paper/2109.05093/) (2021)
- [UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models](https://mlsanity.com/paper/2201.05966/) (2022)
- [Local Explanation of Dialogue Response Generation](https://mlsanity.com/paper/2106.06528/) (2021)

## Built on by (most cited first, 1 of 1)

- [Understanding the Effect of Algorithm Transparency of Model Explanations in Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2410.16283/) (2024)

## Abstract (arXiv)

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.