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Graph Convolution Simulator (GCS)

Source code for "What Has Been Enhanced in my Knowledge-Enhanced Language Model?"

Our GCS probe is fairly simple to use. All you need to prepare are: knowledge graph; entity representations of base LM and enhanced LM.

Below is a demo. We have prepared a toy knowledge graph and its entity representations of RoBERTa and K-Adapter. Here is the visualized pipeline and interpretation results for the toy example:

image

image

As proved in our paper, GCS works stably, and there is no need to fix the random seeds. Users can try multiple times with similar conclusions: For example, the knowledge triple (OR, r, Logic) is successfully integrated during the enhancement.

Try to play it by yourself

Requirements:

PyTorch and DGL should be installed based on your system. For other libraries, you can install them using the following command:

$ pip install -r requirements.txt

Run Knowledge Integration Interpretation by using GCS on example data:

$ bash run_example.sh

Interpretation results are saved in ./example/example_data/gcs.edgelist.

If the knowledge graph is small, users can visualize it by ./example/example_data/results.pdf.

Run GCS for your own Knowledge Integration Interpretation

Only need 2 steps for data preparation, and 1 step for interpretation. Then, you can analyze your results!

Step 1: Prepare the entity embedding of vanilla LM and knowledge-enhanced LM:

Store them as PyTorch tensor (.pt) format. Make sure they have the same number of rows, and the indexes of entities are the same. The default files are emb_roberta.pt and emb_kadapter.pt.

Step 2: Prepare the knowledge graph:

Three files are needed to load the knowledge graph:

  • a) qid2idx.json: The index dictionary. The key is entity Q-label, and value is the index of entity in entity embedding
  • b) qid2label.json : The label dictionary. The key is entity Q-label, and the value is the entity label text. Note that this dictionary is only for visualization, you can set it as {Q-label: Q-label} if you don't have the text.
  • c) kg.edgelist: The knowledge triple to construct knowledge graph. Each row is for one triple as: entity1_idx \t entity2_idx \t {}.

Step 3: Run GCS for KI interpretation:

After two preparation steps, you can run GCS by:

$ python src/example.py  --emb_vlm emb_roberta.pt  --emb_klm emb_kadapter.pt  --data_dir ./example_data  --lr 1e-3  --loss mi_loss

As for the hyperparameters, users may check them in ./example/src/example.py. Note that for large knowledge graphs, we recommend to use mutual information loss (mi_loss), and please do not visualize the results for large knowledge graphs.

Step 4: Analyze GCS interpretation results:

The interpretation results are saved in ./example/example_data/gcs.edgelist. Each row is for one triple as: entity1_idx \t entity2_idx \t {'a': xxxx}. Here, the value of 'a' is the attention coefficient value on the triple/entity (entity1, r, entity2). Users may use them to analyze the factual knowledge integration.

Reproduce the results in the paper

Please enter ./all_exp folder for more details

Cite

If you use the GCS probe or the code, welcome to cite our paper:

@article{hou2022understanding,
  title={What Has Been Enhanced in my Knowledge-Enhanced Language Model?},
  author={Hou, Yifan and Fu, Guoji and Sachan, Mrinmaya},
  journal={arXiv preprint arXiv:2202.00964},
  year={2022}
}

Contact

Feel free to open an issue or send me (yifan.hou@inf.ethz.ch) an email if you have any questions!