This repository contains the codes for the EMNLP 2023 Findings paper, FinePrompt. Our work deals with the idea of transferring task-specific inductive biases from finetuned models to prompts, as a way of improving GPT-4โs compositional reasoning capabilities.
Since this work only deals with the effective prompting on GPT-4, our work requires the installation of the openai
API.
pip install openai
python main.py --bias_type <bias_type_name> --dataset <dataset_name>
where --bias_type
can be one of [genbert
, numnet
, decomprc
, quark
, sae
, qdgat
] and --dataset
can be one of [drop
, musique
]
The set of base and in-context prompts are provided in the prompts.py
.
@inproceedings{kim2023fineprompt,
author={Jeonghwan Kim and Giwon Hong and Sung-Hyon Myaeng and Joyce Jiyoung Whang},
title={FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in {GPT}-4},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2023},
year={2023},
pages={3763--3775}
}