| ## 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 |
| --- |
