Summary of Targa: Targeted Synthetic Data Generation For Practical Reasoning Over Structured Data, by Xiang Huang et al.
TARGA: Targeted Synthetic Data Generation for Practical Reasoning over Structured Data
by Xiang Huang, Jiayu Shen, Shanshan Huang, Sitao Cheng, Xiaxia Wang, Yuzhong Qu
First submitted to arxiv on: 27 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 paper proposes Targeted Synthetic Data Generation (TARGA), a framework for generating synthetic data to improve semantic parsing models’ performance and generalizability. The method starts by identifying relevant entities and relations in a given question, then generates natural language questions as demonstrations for in-context learning. Experimental results show that TARGA outperforms existing non-fine-tuned methods on multiple knowledge base question answering (KBQA) datasets, achieving notable improvements in F1 scores. Additionally, TARGA exhibits superior sample efficiency, robustness, and generalization capabilities under non-I.I.D. settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help computers understand natural language questions better. Right now, it’s hard to get computers to understand questions that they’ve never seen before. To solve this problem, the researchers developed a system called TARGA (Targeted Synthetic Data Generation). This system creates fake examples of questions and answers that are related to the question being asked. The computer then uses these fake examples to learn how to answer similar questions in the future. The results show that TARGA works really well and can help computers understand new questions better than current methods. |
Keywords
» Artificial intelligence » Generalization » Knowledge base » Question answering » Semantic parsing » Synthetic data
