## About
This is a curated subset of 3 representative samples per class (77 classes in total) for the Banking77 dataset, as collected by a domain expert.
It was used in the paper "Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking", published in ACM ICAIF 2023 (https://arxiv.org/abs/2311.06102).
Our findings show that Few-Shot Text Classification on representative samples are better than randomly selected samples.

## Citation

```
@inproceedings{10.1145/3604237.3626891,
author = {Loukas, Lefteris and Stogiannidis, Ilias and Diamantopoulos, Odysseas and Malakasiotis, Prodromos and Vassos, Stavros},
title = {Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking},
year = {2023},
isbn = {9798400702402},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3604237.3626891},
doi = {10.1145/3604237.3626891},
pages = {392–400},
numpages = {9},
keywords = {Anthropic, Cohere, OpenAI, LLMs, NLP, Claude, GPT, Few-shot},
location = {Brooklyn, NY, USA},
series = {ICAIF '23}
}
```

---
language:
- en
Tags:
- banking77
- classification
- conversational
---