LANGCHAIN — Auto Eval of Question Answering Tasks

Any fool can write code that a computer can understand. Good programmers write code that humans can understand. — Martin Fowler.

Auto-evaluating question-answering tasks is an essential aspect of natural language processing. In this tutorial, we will explore a simple tool for evaluating question-answering chains, called auto-evaluator. This tool automates the process of evaluating question-answering models and allows users to explore different configurations and scoring mechanisms.

Setting up Auto-Evaluator

The auto-evaluator tool can be used through a Streamlit app, where users can input a set of documents and optionally provide question-answer pairs. If the user does not supply question-answer pairs, the app will automatically generate an evaluation set using the QAGenerationChain from Langchain. The tool prompts the user to input various configurations for the question-answering chain, including document retrievers, document split methods, split sizes, split overlap, and the language model used for answer summarization.

Evaluating the QA Chain

Once the chain is configured, the app utilizes an LLM (GPT-3.5-turbo) to generate question-answer pairs from the documents and then use the configured chain to generate responses to each question. The responses are then scored by the LLM to measure their quality relative to the provided answers. The tool also allows users to explore scoring across various chain configurations.

Scoring and Comparison

The scoring process involves using LLMs to assess the quality of the retrieved documents and the quality of the answers relative to the evaluation set. These scores are exposed for human inspection, and a descriptive prompt can be used to request a detailed explanation of the assessment from the LLM grader. The experimental results are accumulated for easy comparison across various tests, and a table and scatter plot of the mean score versus the model latency are generated.

Future Directions

Feedback and contributions are welcome for the auto-evaluator tool. Future directions include incorporating other retrievers and models, improving performance, and extending the tool to automate the process of best chain assembly for different tasks.

In this tutorial, we have explored the auto-evaluator tool for evaluating question-answering chains. By automating the evaluation process and enabling easy exploration of various configurations and scoring mechanisms, this tool is a valuable asset for natural language processing tasks.

# Example code for initializing the auto-evaluator tool using Streamlit

import streamlit as st

def main():
    st.title('Auto-Evaluator for Question-Answering Tasks')
    documents = st.text_area("Enter a set of documents")
    # Add input fields for other parameters and configurations
    # ...
    st.button('Evaluate')

if __name__ == '__main__':
    main()

# Example code for using the auto-evaluator to evaluate a question-answering chain

from auto_evaluator import AutoEvaluator

# Initialize the AutoEvaluator with documents and configurations
auto_eval = AutoEvaluator(documents, configurations)
auto_eval.generate_question_answer_pairs()
auto_eval.create_question_answering_chain()
auto_eval.generate_responses()
auto_eval.score_responses()
auto_eval.compare_results()

The auto-evaluator tool provides a streamlined approach to evaluating question-answering chains and offers valuable insights into the performance of different configurations. By leveraging LLMs for scoring and comparison, users can make informed decisions about the effectiveness of their question-answering models.