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Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model

Parishad BehnamGhader, Santiago Miret, Siva Reddy

This repository contains the source code for the evaluations in Can Retriever-Augmented Language Models Reason? The Blame Game Between the Retriever and the Language Model.

Preparing Datasets

This section includes the code for preparing datasets for both QA and LM experiments.

Dependencies

Preparation

You can use the following script to generate QA and LM json datastores for EntailmentBank and StrategyQA datasets. Note that in the experiments on StrategyQA, we have changed the question and answers to a declarative format to perform LM.

cd data
python prepare_data.py --input_file raw/entailmentbank/task_1/dev.jsonl --output_file entailmentbank_1_dev --dataset entailmentbank --qa 1 --lm 1
python prepare_data.py --input_file raw/strategyqa/strategyqa_declarative_train.json --output_file strategyqa --dataset strategyqa --split 1 --lm 1

If you wish to use your own data samples, you must follow the following json data format:

QA

[
  {
    "question": "Which event occurs on a daily cycle?", 
    "answer": ["The Sun rises and sets."], %(for cases like StrategyQA, the answer would be like ["yes", "no"] with "yes" being the correct answer)
    "facts": ["The sun rising / setting occurs once per day."],
    "gold_facts": ["The sun rising / setting occurs once per day."], %(used when evaluating the models with only ground-truth facts)
    "hypothesis": "The sun rising and setting is the event that occurs once per day." %(used when evaluating the models with one single hypothesis sentence.)
  },
]

LM

[
  {
    "query": "As the distance of the star to earth decreases, the [MASK] will appear brighter.",
    "target": ["star", "space"], %(the first alternative target is the ground-truth masked entity)
    "facts": ["A star produces light and heat."], 
    "gold_facts":["A star produces light and heat."] %(used when evaluating the models with only ground-truth facts)
  },
]

Experiments

We evaluate the reasoning abilities of the following retriever-augmented language models:

  1. REALM
  2. kNN-LM
  3. DPR + FiD
  4. Contriever + ATLAS
  5. DPR + Flan-T5
  6. Contriever + Flan-T5/GPT-3.5 in a DSP framework

You may find the visualization of the results in tests/visualization.ipynb. The dependencies for each model is mentioned briefly. You may want to look at each model's repository for more information.

<details><summary>1. REALM</summary> <p>
Dependencies

You may want to use realm/environment.yml as well.

Experiments

The following scripts run all kinds of experiments

cd realm

#QA
python evaluate_reasoning.py \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_task qa \
  --reason_k 5 \
  --reason_dataset <entailmentbank / strategyqa>
  
#LM (target ranking)
python evaluate_reasoning.py \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_task lm \
  --reason_k 5 \
  --reason_dataset <entailmentbank / strategyqa>

A list of the script arguments is explained below:

</p></details> <details><summary>2. kNN-LM</summary> <p>
Dependencies

You may want to use knnlm/environment.yml as well.

Experiments

In order to run the kNN-LM experiments, you must first download the checkpoint provided in the paper's code repository, prepare the datastores and dictionary. The following scripts run all kinds of experiments

cd knnlm

#QA
python evaluate_reasoning.py data-bin/wikitext-103 \
  --path checkpoints/checkpoint_best.pt --sample-break-mode complete \
  --max-tokens 3072 --context-window 2560 --softmax-batch 1024 \
  --model-overrides "{'knn_keytype': 'last_ffn_input'}" --knn-keytype 'last_ffn_input' \
  --knnlm --k 5 --lmbda 0.65 \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_task qa \
  --reason_dataset <entailmentbank / strategyqa>
  
#LM
python evaluate_reasoning.py data-bin/wikitext-103 \
  --path checkpoints/checkpoint_best.pt --sample-break-mode complete \
  --max-tokens 3072 --context-window 2560 --softmax-batch 1024 \
  --model-overrides "{'knn_keytype': 'last_ffn_input'}" --knn-keytype 'last_ffn_input' \
  --knnlm --k 5 --lmbda 0.65 \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_task lm \
  --reason_dataset <entailmentbank / strategyqa>

A list of the script arguments is explained below:

</p></details> <details><summary>3. DPR + FiD</summary> <p>
Dependencies

You may want to use dpr/fid_environment.yml as well.

Experiments

In order to run the fid experiments, you must first prepare the DPR retriever. In these experiments, we load the nq_retriever checkpoints. Also, we use the nq_reader_base checkpoint for the FiD model available in the model's github repository. The following scripts run all kinds of experiments. dpr arguments refer to the retriever's arguments, lm arguments refer to the model's arguments, and reason arguments refer to the specific arguments for our experiments.

cd dpr

#QA
python evaluate_reasoning.py \
  dpr.model_file=<absolute address of the retriever .cp file> \
  lm.model_path=<absolute address of the pretrained reader directory> \
  lm.per_gpu_batch_size=1 lm.name=reason lm.checkpoint_dir=checkpoint \
  reason.data_file=<absolute address of the preprocessed json data file> \
  reason.output_file=<absolute address of a report.jsonl file> \
  reason.k=5 \
  reason.task=qa \
  reason.dataset=<entailmentbank / strategyqa>

  
#LM
python evaluate_reasoning.py \
  dpr.model_file=<absolute address of the retriever .cp file> \
  lm.model_path=<absolute address of the pretrained reader directory> \
  lm.per_gpu_batch_size=1 lm.name=reason lm.checkpoint_dir=checkpoint \
  reason.data_file=<absolute address of the preprocessed json data file> \
  reason.output_file=<absolute address of a report.jsonl file> \
  reason.k=5 \
  reason.task=lm \
  reason.dataset=<entailmentbank / strategyqa>

A list of the script arguments is explained below:

</p></details> <details><summary>4. Contriever + ATLAS</summary> <p>
Dependencies

You may want to use contriever/contriever_environment.yml as well.

Experiments

In order to run the ATLAS experiments, you must first download the preferred model from ATLAS github. In our experiments we load the models/atlas_nq/base ATLAS model. The following scripts run all kinds of experiments.

cd contriever
port=$(shuf -i 15000-16000 -n 1)

#QA
python evaluate_atlas_reasoning.py \
  --generation_max_length 16 --name reason --precision fp32 --text_maxlength 512 \
  --reader_model_type google/t5-base-lm-adapt \ # architecture of Atlas
  --model_path <address to the model checkpoint - atlas_data/models/...> \
  --per_gpu_batch_size 1 --checkpoint_dir atlas_data/experiments --main_port $port \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_k 5 \
  --reason_task qa \
  --reason_dataset <entailmentbank / strategyqa>
  
#LM
python evaluate_atlas_reasoning.py \
  --generation_max_length 16 --name reason --precision fp32 --text_maxlength 512\
  --reader_model_type google/t5-base-lm-adapt \ # architecture of Atlas
  --model_path <address to the model checkpoint - atlas_data/models/...> \
  --per_gpu_batch_size 1 --checkpoint_dir atlas_data/experiments --main_port $port \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_k 5 \
  --reason_task lm \
  --reason_dataset <entailmentbank / strategyqa>

A list of the script arguments is explained below:

</p></details> <details><summary>5. Contriever + Flan-T5</summary> <p>
Dependencies

You may want to use contriever/contriever_environment.yml as well.

Experiments

In order to run the flan-t5 experiments, you must first download the preferred model from ATLAS github. In our experiments we load the models/atlas_nq/base ATLAS model. The following scripts run all kinds of experiments.

cd contriever
port=$(shuf -i 15000-16000 -n 1)

#QA
python evaluate_flan_reasoning.py \
  --generation_max_length 16 --name reason --precision fp32 --text_maxlength 512 \
  --reader_model_type google/t5-base-lm-adapt \ # architecture of Atlas
  --model_path <address to the model checkpoint - atlas_data/models/...> \
  --per_gpu_batch_size 1 --checkpoint_dir atlas_data/experiments --main_port $port \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_k 5 \
  --reason_task qa \
  --reason_k_type k \
  --reason_dataset <entailmentbank / strategyqa>
  --reason_lm <google/flan-t5-small, google/flan-t5-base, google/flan-t5-large, google/flan-t5-xl, google/flan-t5-xxl>
  
#LM
python evaluate_flan_reasoning.py \
  --generation_max_length 16 --name reason --precision fp32 --text_maxlength 512\
  --reader_model_type google/t5-base-lm-adapt \ # architecture of Atlas
  --model_path <address to the model checkpoint - atlas_data/models/...> \
  --per_gpu_batch_size 1 --checkpoint_dir atlas_data/experiments --main_port $port \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_k 5 \
  --reason_task lm \
  --reason_dataset <entailmentbank / strategyqa>
  --reason_lm <google/flan-t5-small, google/flan-t5-base, google/flan-t5-large, google/flan-t5-xl, google/flan-t5-xxl>

A list of the script arguments is explained below:

</p></details> <details><summary>6. Demonstrate-Search-Predict</summary> <p>
Dependencies

You may want to use contriever/dsp_environment.yml as well.

Experiments

First download the preferred model from ATLAS github. In our experiments we load the models/atlas_nq/base ATLAS model.

cd contriever
port=$(shuf -i 15000-16000 -n 1)

#QA
python evaluate_dsp_reasoning.py \
  --generation_max_length 16 --name reason --precision fp32 --text_maxlength 512 \
  --reader_model_type google/t5-base-lm-adapt \ # architecture of Atlas
  --model_path <address to the model checkpoint - atlas_data/models/...> \
  --per_gpu_batch_size 1 --checkpoint_dir atlas_data/experiments --main_port $port \
  --reason_data_file <absolute address of the preprocessed json data file> \
  --reason_output_file <absolute address of a report.jsonl file> \
  --reason_k 5 \
  --reason_task qa \
  --reason_dataset <entailmentbank / strategyqa> \
  --reason_lm <google/flan-t5-base, google/flan-t5-xxl, text-davinci-002> \
  --reason_openai_key <the openai key>

A list of the script arguments is explained below:

</p></details>

In order to reproduce the visualizations in the paper, please run tests/create_visualization_data.py to export the results. Then, you might want to copy the results to the tests/visualization.ipynb.