Awesome

Code for GenRead: Genrate rather than Retrieve!

Introduction & Setup

This is the official implementation of our pre-print paper "Generate rather than Retrieve: Large Language Models are Strong Context Generators", in ICLR 2023 [OpenReview] [arXiv].
Create an environment and install openai package via pip install openai.
Add your OpenAI API key at openai.api_key (line 12) in inference.py

Download the Datasets

From their official websites: [NQ/TriviaQA/WebQ] / [FM2] / [FEVER/Wizard]
From Google drive: (we unified the formats of the above datasets) [link]
Please put them into indataset folder. Now it contains webq and fm2.

Zero-shot Setting

Step1: generate background document.

python mainfunc.py 
  --dataset {dataset} 
  --task step1 
  --split test

Note: we use the text-davinci-002 in our experiment; we use greedy search in the zero-shot setting, to ensure the reproducibility of our experiments.
Note: if you have limited access to OpenAI API, you could directly use our outputs, without spending money on reproducing our experiments. [zero-shot: step1]

Step2: infer answer from document.

python mainfunc.py 
  --dataset {dataset} 
  --task step2 
  --split test

Trick: we remove the \n in the generated documents.
Note: if you have limited access to OpenAI API, you could directly use our outputs, without spending money on reproducing our experiments. [zero-shot: step2]

Supervised Setting

Method1: use sampling to generate multiple documents.

python mainfunc.py 
  --dataset {dataset} 
  --task step1 
  --split test 
  --num_sequence 10 
  --temperature 0.95

We note that when decoding with sample-based methods, the outputs may be different each time. So we cannot guarantee that your output will be exactly the same as the one we provide. [supervised: sampling]

Method2: use clustering to generate diverse documents.

python clusterfunc.py 
  --dataset {dataset} 
  --task step1 
  --split {split} 
  --num_sequence 1 
  --temperature 0.95 
  --clustering

We note that when using different in-context demonstrations, the outputs may be different each time. So we cannot guarantee that your output will be exactly the same as the one we provide. [supervised: clustering]

Fusion-in-decoder: train a reader model to infer answer from documents

We use the FiD code from its official GitHub repository [link].
Download our trained FiD checkpoint at Huggingface Hub.
- GenRead-3B-NQ, performance on NQ test: 45.55
```
git lfs install
git clone https://huggingface.co/wyu1/GenRead-3B-NQ
```
- GenRead-3B-TQA, performance on TQA test: 71.55
```
git lfs install
git clone https://huggingface.co/wyu1/GenRead-3B-TQA
```
If you need checkpoints on other settings, please email wyu1@nd.edu

Citation

@inproceedings{yu2023generate,
  title={Generate rather than retrieve: Large language models are strong context generators},
  author={Yu, Wenhao and Iter, Dan and Wang, Shuohang and Xu, Yichong and Ju, Mingxuan and Sanyal, Soumya and Zhu, Chenguang and Zeng, Michael and Jiang, Meng},
  booktitle={International Conference for Learning Representation (ICLR)},
  year={2023}
}

Please kindly cite our paper if you find this paper and the codes helpful.