Summary of Cot-driven Framework For Short Text Classification: Enhancing and Transferring Capabilities From Large to Smaller Model, by Hui Wu et al.
CoT-Driven Framework for Short Text Classification: Enhancing and Transferring Capabilities from Large to Smaller Model
by Hui Wu, Yuanben Zhang, Zhonghe Han, Yingyan Hou, Lei Wang, Siye Liu, Qihang Gong, Yunping Ge
First submitted to arxiv on: 6 Jan 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 This research paper explores the challenges in Short Text Classification (STC) and proposes innovative methods to enhance its capabilities. The authors highlight the limitations of traditional pre-trained language models and Graph Convolutional Networks in grasping semantic and syntactic intricacies. They also discuss the recent advancements in Large Language Models (LLMs) and Chain-of-Thought (CoT), which have improved complex reasoning tasks but still face limitations. To address these challenges, the authors propose the Syntactic and Semantic Enrichment CoT (SSE-CoT) method, a four-step approach that decomposes STC tasks into concept identification, common-sense knowledge retrieval, text rewriting, and classification. Additionally, they introduce the CoT-Driven Multi-Task Learning (CDMT) framework to extend these capabilities to smaller models by extracting rationales from LLMs and fine-tuning them for optimal performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us better understand and classify short texts on digital platforms. It shows how traditional language models and graph networks struggle with complex text meanings, but newer Large Language Models (LLMs) and Chain-of-Thought (CoT) have improved their abilities. The researchers create a new method called Syntactic and Semantic Enrichment CoT (SSE-CoT), which breaks down the task into four steps: finding important ideas, getting common sense knowledge, rewriting text, and classifying it. They also develop another tool called CoT-Driven Multi-Task Learning (CDMT) to make smaller models better at this task. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Multi task » Text classification
