dexter-cqa 1.0.9

A Benchmark for Complex Heterogeneous Question answering

DEXTER (Benchmarking Complex QA)

Answering complex questions is a difficult task that requires knowledge retrieval. To address this, we propose our easy to use and extensible benchmark composing diverse complex QA tasks and provide a toolkit to evaluate zero-shot retrieval capabilities of state-of-the-art dense and sparse retrieval models in an open-domain setting. Additionally, since context-based reasoning is key to complex QA tasks, we extend our toolkit with various LLM engines. Both the above components together allow our users to evaluate the various components in the Retrieval Augmented Generation pipeline.

For components in retrieval we draw inspiration from BEIR (https://github.com/beir-cellar/beir) and reuse some parts of implementation with modification suited to our setup. We thank the authors for open-sourcing their code.

Colab notebook

Setup (from source)

1. Clone the repo
   
2. Create a conda environment conda create -n bcqa
   
3. pip install -e .
   

From pip

pip install dexter-cqa

Datasets

Dataset Name	Dataset alias	Homepage	Characteristics	#Questions	Corpus Size
MusiqueQA	musiqueqa (2-hop only)	Link	Connected multi-hop reasoning	16.8k	570k
WikiMultiHopQA	wikimultihopqa	Link	Comparative multi-hop reasoning	190k	570k
StrategyQA	strategyqa	Link	Multi-hop reasoning, Implicit Reasoning	2.7k	26.6M
AmbigQA	ambignq	Link	Ambiguous Questions	12k	24.3M
OTT-QA	ottqa	Link	Table and Text multi-hop reasoning	2.1k	6.5M
TAT-QA	tatqa	Link	Financial Table and Text multi-hop reasoning	2.9k	7000
FinQA	finqa	Link	Financial Table and Text multi-hop reasoning	8k	24.8k

Important!! All datasets can be found at one place at Datasets

Retrievers

Name	Paradigm	More
BM25	Lexical	Link
SPLADE	Sparse	Link
DPR	Dense	Link
ANCE	Dense	Link
tas-b	Dense	Link
MPNet	Dense	Link
Contriever	Dense	Link
ColBERTv2	Late-Interaction	Link

Retrieving over large corpus collections

Since some of the datasets have corpus collection with large sizes (millions), we also support chunking of corpus when doing retrieval. To avoid storing docs in memory inspired by the issue https://github.com/beir-cellar/beir/pull/117 we maintain a list of top-k docs with scores when computing scores chunkwise using heapq.

LLM Engines

Project Structure

• data
  • datastructures: Basic data classes for question, answer and others needed in the pipeline.
  • dataloaders: Loaders that take raw json/zip file data and convert them to the format needed in the pipeline
• retriever: Retrievers that take the data loaders and perform retrieval to produce results.
  • dense : dense retrievers like ColBERTv2,ANCE, Contriever, MpNet, DPR and Tas-B
  • lexical: lexical retrievers like BM25
  • sparse: Sparse retrievers like SPLADE
• llms: LLM engine orchestrator and implementation for inference using LLama2, Mistral, OpenAI models and Flan-T5 ( more models to come soon.)
• config: Configuration files with constants and initialization.
• tests: test cases for the above components
• utils: utilities needed in the pipeline like retrieval accuracy calculation and matching.

Running Evaluation

Below is an example script demonstrating how to load a dataset from our benchmark (ambignq here), feed it into one of our retrievers(ANCE here), and evaluate the retrieval quality against the relevance labels provided by the dataset.

from dexter.config.constants import Split
from dexter.data.loaders.RetrieverDataset import RetrieverDataset
from dexter.retriever.dense.ANCE import ANCE
from dexter.utils.metrics.SimilarityMatch import CosineSimilarity
from dexter.utils.metrics.retrieval.RetrievalMetrics import RetrievalMetrics

if __name__ == "__main__":
    # Ensure in config.ini the path to the raw data files are linked under [Data-Path]
    # ambignq = '<path to the data file>
    # ambignq-corpus = '<path to the corpus file>'

    # You can set the split to one of Split.DEV, Split.TEST or Split.TRAIN
    # Setting tokenizer=None only loads only the raw data processed into our standard data classes, if tokenizer is set, the data is also tokenized and stored in the loader.
    loader = RetrieverDataset("ambignq","ambignq-corpus",
                               "config.ini", Split.DEV,tokenizer=None)

    # Initialize your retriever configuration
    config_instance = DenseHyperParams(query_encoder_path="facebook/contriever",
                                     document_encoder_path="facebook/contriever"
                                     ,batch_size=32,show_progress_bar=True)

    # From data loader loads list of queries, corpus and relevance labels.
    queries, qrels, corpus = loader.qrels()

    #Perform Retrieval
    contrvr_search = Contriever(config_instance)   
    similarity_measure = CosineSimilarity()
    response = contrvr_search.retrieve(corpus,queries,100,similarity_measure,chunk=True,chunksize=400000)


    #Evaluate retrieval metrics
    metrics = RetrievalMetrics(k_values=[1,10,100])
    print(metrics.evaluate_retrieval(qrels=qrels,results=response))

Running Evaluation for Results in Paper

All evaluation scripts dataset wise can be found in the evaluation folder

Example TAT-QA ( When building from source)

curl https://gitlab.tudelft.nl/venkteshviswan/bcqa_data/-/raw/main/tatqa.zip -o tatqa.zip

In evaluation/config.ini configure the corresponding paths to downloaded files configure project root directory to PYTHONPATH variable

export PYTHONPATH=/path

export OPENAI_KEY=<your openai key>

export huggingface_token = <your huggingface token to access llama2  >

To reproduce dpr results run

python3 evaluation/tatqa/run_dpr_inference.py

To reproduce colbert results run

python3 evaluation/tatqa/test_tctcolbert_inference.py

Similarly other retrievers can be also run using other scripts in the folder

To Reproduce LLm Results

export OPENAI_KEY="<you key here>"

To run openAI model using colbert docs, run:

python3 evaluation/tatqa/llms/run_rag_few_shot_cot.py

Above experiment would help get numbers for FEW-SHOT-COT for gpt-3.5-turbo which can be checked with Table 3.

Building your own custom dataset

You can quickly build your own dataset in three steps:

1) Loading the question, answer and evidence records

The base data loader by default takes a json file of the format

[{'id':'..','question':'..','answer':'..'}]

Each of the train, test and val splits should under their own json files named under your dir

• /dir_path/train.json
• /dir_path/test.json
• /dir_path/validation.json

If you want to create your custom loader: Within the directory data/dataloaders, Create your Dataloader by extending from BaseDataLoader

class MyDataLoader(BaseDataLoader):
    def load_raw_dataset(self,split):
        dataset = self.load_json(split)
        
        records =  '''your code to transform the elements in json to List[Sample(idx:str,question:Question,answer:Answer,evidence:Evidence)]'''
        # If needed you can also extend from Question,Answer and Evidence dataclasses to form your own types
        self.raw_data = records
    def load_tokenized(self):
        ''' If required overwrite this function to build custom tkenization method of your dataset '''

Under config.ini:

my-dataset = 'dir_path'

1) Loading the corpus

To load your own corpus you can provide a json file of the standard format:

{"idx":{"text":"...","title":"..",'type":"table/text"}}

Under config.ini add:

my-dataset-corpus = '< path to the json file of above format >'

3) Add your dataset alias to constants

Within config.constants:

class Dataset:
    AMBIGQA = "ambignq"
    WIKIMULTIHOPQA = "wikimultihopqa"
    ...
    MY_DATASET = "my-dataset"

and within data/loader/DataLoaderFactory.py:

   def create_dataloader(
...
        if Dataset.AMBIGQA in dataloader_name:
            loader = AmbigQADataLoader
        elif Dataset.FINQA in dataloader_name:
            loader = FinQADataLoader
        ..
        elif Dataset.MY_DATASET in dataloader_name:
            loader = MyDataLoader
    

Your dataset is now ready to be loaded and used.

a) You can load the dataloader as:

loader_factory = DataLoaderFactory()
loader = loader_factory.create_dataloader("my-dataset", config_path="config.ini", split=Split.DEV, batch_size=10)

b) You can load the corpus as:

loader = PassageDataLoader(dataset="my-dataset-corpus",subset_ids=None,config_path="config.ini",tokenizer=None)

c) You can load RetrieverDataset as:

loader = RetrieverDataset("my-dataset","my-dataset-corpus",
                               "config.ini", Split.DEV,tokenizer=None)

Bulding your own retrievers

To build your own retriever you can extend from the class bcqa/retriever/BaseRetriever.py and use it in your evaluation script.

Citing & Authors

Thanks to the following collaborators:
Venktesh Viswanathan
Deepali Prabhu
Avishek Anand