Summary of Digestion Algorithm in Hierarchical Symbolic Forests: a Fast Text Normalization Algorithm and Semantic Parsing Framework For Specific Scenarios and Lightweight Deployment, by Kevin You
Digestion Algorithm in Hierarchical Symbolic Forests: A Fast Text Normalization Algorithm and Semantic Parsing Framework for Specific Scenarios and Lightweight Deployment
by Kevin You
First submitted to arxiv on: 18 Dec 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 proposed multilayer framework, Digestion Algorithm in Hierarchical Symbolic Forests (DAHSF), aims to address interpretability issues in Large Language Models (LLMs) by combining text normalization and semantic parsing workflows. This addresses limitations such as poor model credibility, data scarcity, catastrophic forgetting, and lengthy response times. The DAHSF algorithm is inspired by the multiplication rule and human thinking patterns, enabling local deployment on small datasets with optimized model size and memory usage. The technology is tested and applied on the Chinese Scripting Language “Fire Bunny Intelligent Development Platform V2.0”. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to make language models more understandable and useful. It combines two important tasks: making text easier to understand (text normalization) and understanding the meaning of words and sentences (semantic parsing). This helps address problems like poor model credibility, limited data, and slow response times. The new algorithm is inspired by simple math rules and how humans think, allowing it to run on small datasets and be used locally. |
Keywords
» Artificial intelligence » Semantic parsing
