What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition

Paper Abstract

The knowledge encapsulated in a model is the core factor determining its final performance on downstream tasks. Much research in NLP has focused on efficient methods for storing and adapting different types of knowledge, e.g., in dedicated modularized structures, and on how to effectively combine these, e.g., by learning additional parameters. However, given the many possible options, a thorough understanding of the mechanisms involved in these compositions is missing, and hence it remains unclear which strategies to utilize. To address this research gap, we propose a novel framework for zero-shot module composition, which encompasses existing and some novel variations for selecting, weighting, and combining parameter modules under a single unified notion. Focusing on the scenario of domain knowledge and adapter layers, our framework provides a systematic unification of concepts, allowing us to conduct the first comprehensive benchmarking study of various zero-shot knowledge composition strategies. In particular, we test two module combination methods and five selection and weighting strategies for their effectiveness and efficiency in an extensive experimental setup. Our results highlight the efficacy of ensembling but also hint at the power of simple though often-ignored weighting methods. Further in-depth analyses allow us to understand the role of weighting vs. top-k selection, and show that, to a certain extent, the performance of adapter composition can even be predicted.

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Getting Started

We conducted all our experiments with Python 3.10. Before getting started, make sure you install the requirements listed in the requirements.txt file.

pip install -r requirements.txt

Repository Description

This repository contains all code and data needed to reproduce the experiments and results reported in our paper.

Data

A brief description of the files in data is:

• experimental_results.xlsx

  • Contains all results we produced throughout our experiments.

• efficiency calculation.xlsx

  • Contains the results of the efficiency calculations for DeBERTa and GPT-2.

Additional data sources used:

• C4 Corpus
• yelp.com Corpus

Note: The computed domain adapters could not be uploaded to GitHub due to size constraints. Find it on https://xxxx

Code

Includes all python files and notebooks subject to this paper.

A brief description of the files in code is:

• creation_of_paper_plots.ipynb

  • This notebook can be used to recreate all plots present in the paper, based on the experimental results.

• run_clm_adapter.py

  • This script is based on the run_clm.py script of the Adapter Hub, which can be found here. It is used to train the domain adapters using a causal language modeling loss.

• run_mlm_adapter.py

  • This script is based on the run_mlm.py script of the Adapter Hub, which can be found here. It is used to train the domain adapters using a masked language modeling loss.

Shell Files

Includes example shell files to run the python code.

• train_clm_adapter.sh

  • Contains a shell script to run run_clm_adapter.py and trains a domain adapter using the specified variables.

• train_mlm_adapter.sh

  • Contains a shell script to run run_mlm_adapter.py and trains a domain adapter using the specified variables.

• evaluate_clm_models.sh

  • Contains a shell script to run run_clm_adapter.py and evaluates the adapters using our presented framework.

• evaluate_mlm_models.sh

  • Contains a shell script to run run_mlm_adapter.py and evaluates the adapters using our presented framework.

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References

Please use the following bibtex entry if you use this model in your project (TBD):

@inproceedings{,
    title = "What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition",
    author = "Holtermann, Carolin and Frohmann, Markus and Rekabsaz, Navid and Lauscher, Anne",
    editor = "",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2024",
    year = "2024",
    publisher = "Association for Computational Linguistics",
    url = "",
    doi = "",
    pages = "",
}

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Author contact information: carolin.holtermann@uni-hamburg.de

License

All source code is made available under a MIT license. See LICENSE.md for the full license text.