Evidentiality-guided Generator

This is the official implementation of the following paper: Akari Asai, Matt Gardner and Hannaneh Hajishirzi. Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks. In Proc. NAACL. 2021.

In this paper, we introduce Evidentiality-guided Generator, which incorporates evidentiality of passages---whether a passage contains correct evidence to support the output---into training the generator via multi-task learning of answer generation and evidentiality prediction for retrieval-augmented generation. Experimental results show large improvements across three knowledge intensive tasks: open question answering, fact verification and knowledge-enhanced dialogue.

Directories

• evi_gen: codes for our evidentiality-guided generator model. The implementation is built upon Fusion-in-Decoder (Izacard and Grave, 2020).

• mining: code for our evidentiality labeling model used to obtain silver evidentiality data.

Please see the training and evaluation details in each directories.

Data and Models

Retrieved data (train / dev / test )

We release the DPR retrieved results and the results with our silver evidentiality labels. All of the data can be downloaded from here.

• evidentiality_dpr.zip includes the retrieval results with our newly mined silver evidentiality labels for train sets for each target dataset. For each query, we include top 20 passages.
• eval_dpr.zip includes the retrieval results for dev / test sets for each target dataset.

Fine-tuned models

You can download the fine-tuned models from the google drive repositories.

• NQ Open
• TriviaQA unfiltered
• FaVIQ unfiltered
• FEVER unfiltered
• WoW

Evaluations

To reproduce the original results, you can go to evi_gen directory and then run the command below:

CUDA_VISIBLE_DEVICES=0 python test_reader.py \
    --model_path model/nq_ours \
    --eval_data data/nq_test.json \
    --per_gpu_batch_size 48 \
    --n_context 20 \
    --name sanity_nq_test \
    --checkpoint_dir checkpoint \
    --n_gpus 1 \
    --write_results

For WoW, please set the --metric f1

Training

Overview of Training

Our evidentiality-guided generator will conduct a multi-task learning of evidentiality prediction and generation.
To supervised this learning, we need to obtain silver evidentiality data.

Our training procedures are as follows:

1. Training a base Fusion-in-Decoder model (base generator)
2. Run leave-one-out generation approach to collect training data for evidentiality labeling model (M) using the base generator.
3. Train M using data from step 2.
4. Run M on all of the passages included in training data for a evidentiality-guided generator to obtain silver evidentiality labels.
5. Train the evidentiality-guided generator with the multi-task loss.

See more detailed instructions in the evi_gen and mining directories.

Training our evidentiality generator

If you want to quickly start training our evidentiality-guided generator, we provide the resulting training data here.
To train our evidentiality generator, please run the command below:

python train_reader.py \
    --use_checkpoint --lr 0.00005 --optim adamw \
    --scheduler linear --weight_decay 0.01 \
    --text_maxlength 250 --per_gpu_batch_size 1 \
    --n_context 2 --total_step 120000 \
    --warmup_step 1000 \
    --train_data /path/to/train/data.json \
    --eval_data /path/to/train/data.json \
    --model_size base --name /model_name/ --accumulation_steps 4 \
    --n_gpus 8 --eval_freq 5000 \
    --answer_maxlength 5

Citations and Contact

Please contact Akari Asai (akari[at].cs.washington.edu) for questions or suggestions.

If you use our data/models in your work, please cite our paper.

@inproceedings{asai-etal-2022-evidentiality,
    title = "Evidentiality-guided Generation for Knowledge-Intensive {NLP} Tasks",
    author = "Asai, Akari  and
      Gardner, Matt  and
      Hajishirzi, Hannaneh",
    booktitle = "Proc. NAACL",
    year = "2022",
    url = "https://aclanthology.org/2022.naacl-main.162"
}