# Towards Data-efficient Customer Intent Recognition with Prompt-based Learning Paradigm

arXiv: https://arxiv.org/abs/2309.14779 · HTML: https://mlsanity.com/paper/2309.14779/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [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)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [OpenPrompt: An Open-source Framework for Prompt-learning](https://mlsanity.com/paper/2111.01998/) (2021)

## Built on by (most cited first, 3 of 3)

- [Fine-Tuning Large Language Models for Scientific Text Classification: A Comparative Study](https://mlsanity.com/paper/2412.00098/) (2024)
- [Designing Domain-Specific Large Language Models: The Critical Role of Fine-Tuning in Public Opinion Simulation](https://mlsanity.com/paper/2409.19308/) (2024)
- [Advancing Scientific Text Classification: Fine-Tuned Models with Dataset Expansion and Hard-Voting](https://mlsanity.com/paper/2504.19021/) (2025)

## Abstract (arXiv)

Recognizing customer intent accurately with language models based on customer-agent conversational data is essential in today's digital customer service marketplace, but it is often hindered by the lack of sufficient labeled data. In this paper, we introduce the prompt-based learning paradigm that significantly reduces the dependency on extensive datasets. Utilizing prompted training combined with answer mapping techniques, this approach allows small language models to achieve competitive intent recognition performance with only a minimal amount of training data. Furthermore, We enhance the performance by integrating active sampling and ensemble learning strategies in the prompted training pipeline. Additionally, preliminary tests in a zero-shot setting demonstrate that, with well-crafted and detailed prompts, small language models show considerable instruction-following potential even without any further training. These results highlight the viability of semantic modeling of conversational data in a more data-efficient manner with minimal data use, paving the way for advancements in AI-driven customer service.