kozakroman / genread Goto Github PK

• 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

• 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.

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]

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 [email protected]

@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.