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

arXiv: https://arxiv.org/abs/2311.06102 · HTML: https://mlsanity.com/paper/2311.06102/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 48 references that are in the map.
- First posted on arXiv 10 Nov 2023.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels](https://mlsanity.com/paper/2010.01653/) (2020)
- [Example-Driven Intent Prediction with Observers](https://mlsanity.com/paper/2010.08684/) (2020)
- [Breaking the Bank with ChatGPT: Few-Shot Text Classification for Finance](https://mlsanity.com/paper/2308.14634/) (2023)
- [Azimuth: Systematic Error Analysis for Text Classification](https://mlsanity.com/paper/2212.08216/) (2022)
- [Financial misstatement detection: a realistic evaluation](https://mlsanity.com/paper/2305.17457/) (2023)
- [Learning Better Intent Representations for Financial Open Intent Classification](https://mlsanity.com/paper/2210.14304/) (2022)

## Built on by (most cited first, 30 of 37)

- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2402.07867/) (2024)
- [On-Device Language Models: A Comprehensive Review](https://mlsanity.com/paper/2409.00088/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models](https://mlsanity.com/paper/2406.00083/) (2024)
- [Feedback-Guided Extraction of Knowledge Base from Retrieval-Augmented LLM Applications](https://mlsanity.com/paper/2411.14110/) (2024)
- [RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis](https://mlsanity.com/paper/2411.18948/) (2024)
- [One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2505.11548/) (2025)
- [Data Poisoning for In-context Learning](https://mlsanity.com/paper/2402.02160/) (2024)
- [Corpus Poisoning via Approximate Greedy Gradient Descent](https://mlsanity.com/paper/2406.05087/) (2024)
- [Benchmarking Poisoning Attacks against Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.18543/) (2025)
- [On the Vulnerability of Applying Retrieval-Augmented Generation within Knowledge-Intensive Application Domains](https://mlsanity.com/paper/2409.17275/) (2024)
- [Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off](https://mlsanity.com/paper/2603.26815/) (2026)
- [ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checking](https://mlsanity.com/paper/2510.13842/) (2025)
- [On Fairness of Low-Rank Adaptation of Large Models](https://mlsanity.com/paper/2405.17512/) (2024)
- [Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach](https://mlsanity.com/paper/2407.15788/) (2024)
- [DCMI: A Differential Calibration Membership Inference Attack Against Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.06026/) (2025)
- [LLMs are One-Shot URL Classifiers and Explainers](https://mlsanity.com/paper/2409.14306/) (2024)
- [Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research](https://mlsanity.com/paper/2411.00890/) (2024)
- [POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models](https://mlsanity.com/paper/2505.06579/) (2025)
- [Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2506.04290/) (2025)
- [Pruning Strategies for Backdoor Defense in LLMs](https://mlsanity.com/paper/2508.20032/) (2025)
- [Adaptable and Reliable Text Classification using Large Language Models](https://mlsanity.com/paper/2405.10523/) (2024)
- [A Scalable Data-Driven Framework for Systematic Analysis of SEC 10-K Filings Using Large Language Models](https://mlsanity.com/paper/2409.17581/) (2024)
- [UniC-RAG: Universal Knowledge Corruption Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.18652/) (2025)
- [RAGPart & RAGMask: Retrieval-Stage Defenses Against Corpus Poisoning in Retrieval-Augmented Generation](https://mlsanity.com/paper/2512.24268/) (2025)
- [Connect the Dots: Knowledge Graph-Guided Crawler Attack on Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2601.15678/) (2026)
- [When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes](https://mlsanity.com/paper/2404.12365/) (2024)
- [Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuning](https://mlsanity.com/paper/2409.11408/) (2024)
- [Facilitating Pornographic Text Detection for Open-Domain Dialogue Systems via Knowledge Distillation of Large Language Models](https://mlsanity.com/paper/2403.13250/) (2024)
- [DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers](https://mlsanity.com/paper/2405.16482/) (2024)

## Abstract (arXiv)

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.