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UDR: Unified Demonstration Retriever for In-Context Learning

Implementation of out ACL'23 paper Unified Demonstration Retriever for In-Context Learning.

In this paper, we propose Unified Demonstration Retriever (UDR), a single model to retrieve demonstrations for a wide range of tasks. To train UDR, we cast various tasks’ training signals into a unified listwise ranking formulation by language model’s feedback. Then we propose a multi-task listwise ranking training framework, with an iterative mining strategy to find high-quality candidates, which can help UDR fully incorporate various tasks’ signals.

How to run the code

Dependencies

We use Python 3.8.0 and PyTorch 1.7.1 with cuda 11.0. We recommend installing allennlp and apex first, then you can install other dependencies by running:

pip install -r requirements.txt

Setup Elasticsearch

To speed up the retrieval process, we use Elasticsearch to initialize candidates of each training example.

#### Downloading ####
ES=./elasticsearch-7.9.1/bin/elasticsearch
if test -f "$ES"; then
    echo "$ES exists. Using the existent one"
else 
    echo "$ES does not exist. Downloading a new one"
    wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.9.1-linux-x86_64.tar.gz
    tar -xf elasticsearch-7.9.1-linux-x86_64.tar.gz
fi

#### Starting the ES service ####
nohup ./elasticsearch-7.9.1/bin/elasticsearch > elasticsearch.log &

Training

The iterative training process of UDR can be done by the following commands:

# Initialize candidates of each training example by BM25 in Elasticsearch
bash scripts/find_bm25.sh
# Score initial candidates by language model
bash scripts/score_bm25.sh
# Train UDR with the initial candidates  (iteration 0)
bash scripts/train_iter0.sh

# Update candidates by new retriever got in the last step
bash scripts/dr_iter0.sh
# Score updated candidates by language model
bash scripts/score_iter0.sh
# Train UDR with the updated candidates (iteration 1)
bash scripts/train_iter1.sh

# Update candidates by new retriever got in the last step
bash scripts/dr_iter1.sh
# Score updated candidates by language model
bash scripts/score_iter1.sh
# Train UDR with the updated candidates (iteration 2)
bash scripts/train_iter2.sh

Testing

To test UDR, we can run the following command:

bash scripts/test_iter2.sh

Data

All datasets are uploaded to Huggingface and can be found here.

Citation

If you find this repo useful, please cite the following paper:

@article{li2023unified,
  title={Unified Demonstration Retriever for In-Context Learning},
  author={Li, Xiaonan and Lv, Kai and Yan, Hang and Lin, Tianyang and Zhu, Wei and Ni, Yuan and Xie, Guotong and Wang, Xiaoling and Qiu, Xipeng},
  journal={arXiv preprint arXiv:2305.04320},
  year={2023}
}