Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking

• URL: http://arxiv.org/abs/2311.06102v1
• Date: Fri, 10 Nov 2023 15:10:36 GMT
• Title: Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking
• Authors: Lefteris Loukas, Ilias Stogiannidis, Odysseas Diamantopoulos, Prodromos Malakasiotis, Stavros Vassos
• Abstract summary: Few-shot and large language models (LLMs) can perform effectively with just 1-5 examples per class. Our work addresses the performance-cost trade-offs of these methods over the Banking77 financial intent detection dataset. To inspire future research, we provide a human expert's curated subset of Banking77, along with extensive error analysis.
• Score: 3.9412826185755017
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains. Few-shot methods offer an alternative, utilizing contrastive learning techniques that can be effective with as little as 20 examples per class. Similarly, Large Language Models (LLMs) like GPT-4 can perform effectively with just 1-5 examples per class. However, the performance-cost trade-offs of these methods remain underexplored, a critical concern for budget-limited organizations. Our work addresses this gap by studying the aforementioned approaches over the Banking77 financial intent detection dataset, including the evaluation of cutting-edge LLMs by OpenAI, Cohere, and Anthropic in a comprehensive set of few-shot scenarios. We complete the picture with two additional methods: first, a cost-effective querying method for LLMs based on retrieval-augmented generation (RAG), able to reduce operational costs multiple times compared to classic few-shot approaches, and second, a data augmentation method using GPT-4, able to improve performance in data-limited scenarios. Finally, to inspire future research, we provide a human expert's curated subset of Banking77, along with extensive error analysis.

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