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MS MARCO LLM Distillation

This repository contains the code and data for the paper A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking.

Usage

We use the lightning-ir library for fine-tuning and running experiments. Follow the installation instructions from the repository to install the library.

Model Zoo

Model Name TREC DL 19/20 nDCG@10 (BM25) TIREx nDCG@10
monoelectra-base 0.715 0.416
monoelectra-large 0.730 0.434

Data

The run files for MS MARCO training queries for different first-stage retrieval models and LLM re-rankers can be downloaded from Zenodo.

Fine-tuning

To fine-tune a model using ColBERTv2 hard negatives, update the train_dataset argument in configs/monoelectra-fine-tune-colbert.yaml to point to the downloaded ColBERTv2 run file.

Then run the following command to fine-tune the model:

lightning-ir fit --config configs/monoelectra-fine-tune-colbert.yaml

To fine-tune a model using our proposed distillation method, update the train_dataset argument in configs/monoelectra-fine-tune-distillation.yaml to point to the downloaded distillation run file.

Then run the following command to fine-tune the model:

lightning-ir fit --config configs/monoelectra-fine-tune-distillation.yaml

Inference

We provide an example configuration file for running inference on TREC Deep Learning 2019 and 2020 using our fine-tuned models in configs/predict-trec-dl.yaml.

To re-rank passages, run the following command (this will download the fine-tuned from HuggingFace and the MS MARCO passage corpus using ir_datasets):

lightning-ir re_rank --config configs/predict-trec-dl.yaml

Citation

@article{schlatt:2024,
  author =                {Ferdinand Schlatt and Maik Fr{\"o}be and Harrisen Scells and Shengyao Zhuang and Bevan Koopman and Guido Zuccon and Benno Stein and Martin Potthast and Matthias Hagen},
  doi =                   {10.48550/2405.07920},
  journal =               {CoRR},
  month =                 may,
  title =                 {{A Systematic Investigation of Distilling Large Language Models into Cross-Encoders for Passage Re-ranking}},
  url =                   {https://arxiv.org/abs/2405.07920},
  year =                  2024
}