This repository contains code accompanying our paper "FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning" (ACL 2023) [Paper] [Slides] [Poster].

FiD-ICL is inspired by fusion-in-decoder models designed for open-domain QA. In a meta-training setting, FiD-ICL outperforms the widely-adopted concatenation-based ICL, while being up to 10x faster at inference time. When compared to fine-tuning, the performance gap between FiD-ICL (gradient-free) and fine-tuning (gradient-based) is on average less than 3%.

• Environment
• Data
• ICL Meta-Training
• ICL Meta-Testing
• Other Baselines
• Download Checkpoints
• Contact Us

We have included requirements.txt in this repository. Please run the following:

conda create -n fid-icl python=3.9
conda activate fid-icl
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
pip install -r requirements.txt

We use the following resources in our study:

• Public Pool of Prompts (P3): We use the P3 data as meta-train and meta-test set.
• BIG-bench: We use 14 tasks from BIG-bench as meta-validation to select the best checkpoint during meta-training. We use these 14 tasks because they are also used in the original T0 paper.

Please check out the instructions to download the data in t0/data_prep/README.md and big-bench/data_prep/README.md.

If you only want to download the resulting model and reproduce the evaluation part, you don't need to run meta-training because model checkpoints are ported to huggingface hub. Go to ICL Meta-Testing section below to run evaluation directly.

The following script will train a FiD-ICL model initializing the model with T5-LM-XL (This is the best performing ICL model in the paper).

cd encdec
python run_fid.py -c runs/metatrain/fid_t5_xl.json

Use run_icl.py and run_ensemble.py to meta-train a model with Concat-ICL and Ensemble-ICL. There are sample configurations in the runs/metatrain directory.

The following script will evaluate the FiD-ICL trained from T5-LM-XL.

python run_fid_eval.py -c runs/metatest/fid_t5_xl.json 

Similarly, use run_icl_eval.py and run_ensemble_eval.py to meta-test a model trained with Concat-ICL and Ensemble-ICL. There are sample configurations in the runs/metatest directory.

We include example scripts for running the baselines in this repository.

python run_t0eval.py -c runs/metatest/t0_3b.json
# results will be in eval_logs/t0_eval/my-t0-3b/results_t0_template.csv

python run_t0fewshot.py -c runs/metatest/ft_t0_3b.json

The only difference in experiment setting between us and the original T-Few paper is that we control the number of shots to be 16. Download the T-Few repository (https://github.com/r-three/t-few); Copy and paste the data files in t0/data_fewshot to T-Few directory and change the data path in T-Few code.

Checkpoints are uploaded to huggingface hub. Their model identifier are listed in a table in CHECKPOINTS.md (link).

You can find these models on huggingface hub. If the identifier is qinyuany/my-t0-3b then the webpage is https://huggingface.co/qinyuany/my-t0-3b

To use these models, please run the following:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
identifier = "qinyuany/my-t0-base" # or other model identifier

tokenizer = AutoTokenizer.from_pretrained(identifier)
model = AutoModelForSeq2SeqLM.from_pretrained(identifier)

If you have any question, please submit an issue, or reach out to Qinyuan (qinyuany@usc.edu).