Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations

Paper: Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations

Getting Started

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To run the code, follow these steps:

Environment Setup

1. Clone the repository:
   git clone https://github.com/Shaier/Adaptive_QA.git

2. Navigate to the repository directory:
   cd Adaptive_QA

3. Install the required packages:
   pip install -r requirements.txt

4. Create a new conda environment with Python 3.11:
   conda create -n adaptive_qa python=3.11

5. Activate the environment:
   conda activate adaptive_qa

Datasets

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Datasets can be downloaded from: https://drive.google.com/drive/folders/1gBKf_SmsLoAYiVzDpdJ6Ogasv2FTRnYt?usp=sharing

Alternatively, you can prepare the datasets using the provided notebooks:

• crate_hotpot_qa_cite.ipynb
• create_ambig_qa_cite.ipynb
• create_disent_qa_cite.ipynb

Citation

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If you use the code or paper, please cite us with:

@inproceedings{shaier-etal-2024-adaptive, title = "Adaptive Question Answering: Enhancing Language Model Proficiency for Addressing Knowledge Conflicts with Source Citations", author = "Shaier, Sagi and Kobren, Ari and Ogren, Philip", editor = "Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung", booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing", month = nov, year = "2024", address = "Miami, Florida, USA", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.emnlp-main.956", pages = "17226--17239"}