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TempLM

This repo currently implements the experiments in the paper "TempLM: Distilling Language Models into Template-Based Generators".

Authors:

• Tianyi Zhang
• Mina Lee*
• Xiang Lisa Li*
• Ende Shen*
• Tatsunori Hashimoto

Overview

This repo implements the experiments in the paper "TempLM: Distilling Language Models into Template-Based Generators". TempLM distills pretrained language models into template-based generators to achieve faithfulness and interpretability. For an illustration,

If you find this repo useful, please cite:

@article{Zhang2022TempLMDL,
  title={TempLM: Distilling Language Models into Template-Based Generators},
  author={Tianyi Zhang and Mina Lee and Xiang Lisa Li and Ende Shen and Tatsunori Hashimoto},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.11055}
}

Dependencies

Basic packages are listed in requirements.txt and the full specification is in requirements_full.txt

Data

• E2E text generation dataset: this will be downloaded via the huggingface dataset package. You do not need to download E2E manually. We also extract and sorted the E2E dev and test sets at ./e2e_data.
• SynthBio: this dataset is included with our repo under ./synthbio. We create our own test, train, and validation splits from the original dataset.

Usage

You can also download the all pretrained models (link). The uncompressed folder should look like:

├── e2e
│   ├── delex_bart
│   ├── delex_gt
│   ├── infill_models
│   ├── refine
│   └── seq2seq_models
└── synthbio
    ├── delex_bart
    ├── delex_gt
    ├── infill_models
    ├── refine
    └── seq2seq_models

E2E Experiments

You should either set up environment variables or modify the released scripts to specify paths to data and model checkpoints.

export DIR=<path to this directory>
export OUT=<top level dir of experiment output> # will look like the downloaded folder if you run all the commands detailed below

To run evaluation, you should clone the official E2E evaluation repo. For example,

cd $DIR
git clone https://github.com/tuetschek/e2e-metrics.git

To replicate experiments on the E2E dataset:

1. Finetune a Seq2Seq BART model ./scripts/e2e/fewshot/train_seq2seq.sh
2. Finetune an infilling BART model ./scripts/e2e/fewshot/train_infill.sh
3. Extract templates by delexicializing BART outputs and keep the topk candidates ./scripts/e2e/fewshot/delex_bart_select.sh
4. Refine templates ./scripts/e2e/fewshot/refine.sh
5. Evaluate on test and validation ./scripts/e2e/fewshot/eval_seq2seq.sh
6. Refer to $OUT/e2e/fewshot/s1/dev_output/metrics.txt for the generated metric scores
7. Automatic faithfulness evaluation /scripts/e2e/fewshot/fact_check.sh. Some commands are commented out and can be added back for evaluating other baselines.

For two baselines reported in the paper, delexicalizing training data and all model outputs, replace 3 and 4 with delex_gt.sh or delex_bart.sh. To evaluate the finetuned BART model, refer to ./scripts/e2e/eval_seq2seq.sh.

SynthBio Experiments

Similar to the procedure described above:

1. Finetune a Seq2Seq BART model ./scripts/synthbio/train_seq2seq.sh
2. Finetune an infilling BART model ./scripts/synthbio/train_infill.sh
3. Extract templates by delexicializing BART outputs and keep the topk candidates ./scripts/synthbio/delex_bart_select.sh
4. Refine templates ./scripts/synthbio/refine.sh
5. Evaluate on test and validation ./scripts/synthbio/eval_seq2seq.sh
6. Refer to $OUT/synthbio/s1/dev_output/metrics.txt for the generated metric scores

File structure of this repo

1. *_args.py files organize argument parsers we used in different experiments. *_utils.py contain miscellaneous helper functions.
2. Data related code
   1. data.py implements the TabularData file structure, which implements the clustering of input data tables.
   2. preprocessing.py processes the text descriptions in these datasets into standard formats
   3. field_transformation.py implements auxiliary transfomrations to the input data that enable stronger template systems.
   4. ./datasets/synthbio.py helps load the SynthBio dataset into the huggingface format.
3. Model training code
   1. train_seq2seq.py finetunes a BART model for left to right decoding.
   2. train_infill.py finetunes a BARAT model for infilling.
4. Template extraction code
   1. template_search.py implements template extraction with delexicalization.
   2. template_refinement.py implements refining templates with an infilling model.
   3. template_search_bart.py implements the consensus beam search algorithm for generating templates with an infilling model.
5. Evaluation code
   1. eval_baselines.py for BART decoding
   2. eval_e2e_templates.py for template inference on E2E
   3. eval_synthbio_templates.py for template inference on SynthBio