Summary of Towards Compositionally Generalizable Semantic Parsing in Large Language Models: a Survey, by Amogh Mannekote
Towards Compositionally Generalizable Semantic Parsing in Large Language Models: A Survey
by Amogh Mannekote
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the concept of compositional generalization in large language models (LLMs), which is essential for tasks like task-oriented dialogue, text-to-SQL parsing, and information retrieval. Despite LLMs’ success in various NLP tasks, perfecting compositional generalization remains an open challenge. The research presents a comprehensive survey of recent advances in analyzing, improving, and evaluating the compositional generalization capabilities of LLMs for semantic parsing tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models can understand new combinations of words by just seeing simple words before. This is important for things like talking to computers and understanding natural language. Even though these models are good at many things, they still struggle with this one thing: understanding very complex combinations of words. The researchers made a list of recent discoveries in this area to help others get started. |
Keywords
» Artificial intelligence » Generalization » Nlp » Parsing » Semantic parsing
