Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis

• URL: http://arxiv.org/abs/2410.06550v1
• Date: Wed, 9 Oct 2024 05:15:13 GMT
• Title: Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis
• Authors: Shiho Matta, Yin Jou Huang, Fei Cheng, Hirokazu Kiyomaru, Yugo Murawaki,
• Abstract summary: We show how to balance the trade-off between the higher quality but more expensive human data and the lower quality yet substantially cheaper LLM-generated data. Our experiments, conducted across various budget levels, reveal that optimal cost-efficiency is achieved by combining both human and LLM-generated data.
• Score: 18.44272589315175
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Recent studies have demonstrated that few-shot learning allows LLMs to generate training data for supervised models at a low cost. However, the quality of LLM-generated data may not entirely match that of human-labeled data. This raises a crucial question: how should one balance the trade-off between the higher quality but more expensive human data and the lower quality yet substantially cheaper LLM-generated data? In this paper, we synthesized training data for conversational semantic frame analysis using GPT-4 and examined how to allocate budgets optimally to achieve the best performance. Our experiments, conducted across various budget levels, reveal that optimal cost-efficiency is achieved by combining both human and LLM-generated data across a wide range of budget levels. Notably, as the budget decreases, a higher proportion of LLM-generated data becomes more preferable.

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