Summary of Investigating Cost-efficiency Of Llm-generated Training Data For Conversational Semantic Frame Analysis, by Shiho Matta et al.
Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis
by Shiho Matta, Yin Jou Huang, Fei Cheng, Hirokazu Kiyomaru, Yugo Murawaki
First submitted to arxiv on: 9 Oct 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 paper explores the trade-off between high-quality human-labeled training data and lower-cost Large Language Model (LLM)-generated data for supervised models. Recent studies have shown that few-shot learning enables LLMs to generate training data at a low cost, but the quality may not match human-labeled data. To address this, the authors synthesized training data using GPT-4 for conversational semantic frame analysis and examined how to allocate budgets optimally to achieve the best performance. The experiments, conducted across various budget levels, reveal that combining both human and LLM-generated data achieves optimal cost-efficiency, with a higher proportion of LLM-generated data preferred as the budget decreases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a balance between using expensive human-made training data and cheaper computer-made data for machine learning models. Researchers have shown that computers can make training data quickly and cheaply, but it might not be as good as human-made data. The authors of this paper want to know how to use both types of data together to get the best results. They tested different combinations of human-made and computer-made data at different costs and found that using a mix of both works best. As the cost goes down, they think it’s better to use more computer-made data. |
Keywords
» Artificial intelligence » Few shot » Gpt » Large language model » Machine learning » Supervised
