Rag Fact Checking System

A Python library for validating the factual accuracy of Large Language Model (LLM) responses against their source documents in Retrieval-Augmented Generation (RAG) systems. The raw textual inputs are converted into triplets (subject, predicate, object) to represent the sentences and then fact-checked against the reference documents.

This library offers you sentence-level fact checking granularity, with the possibility to extract the exact incorrect triplet from an LLM response.

The generic way of how it works is as follows:

• Generate the triplets from the answer of the LLM
• Generate the triplets from the reference documents
• Compare the triplets from the LLM answer with the triplets from the reference documents
• If the triplets from the LLM answer are not present in the reference documents, it is a hallucination

NB: This fact-checking system was built and validated in https://huggingface.co/datasets/rag-datasets/rag-mini-bioasq dataset, specifically on the Thyroid topic. There is no guarantee that it will work for other datasets and might need more adjustments to be used for other datasets.

Main Features

There are a couple of separate components in this library:

• Triplet Generator: Extracts factual relationships from the input text in the form of triplets, which consist of a subject, predicate, and object. This is done using LLMs.
• Fact Checker: Compares these triplets with those from a reference text to determine if they match (true/false).
• Hallucination Generator: An LLM generator that generates hallucinated triplets from the reference documents. This is done in order to synthetically generate a dataset of hallucinated triplets.

Installation

This is a pip installable package. To install it, run the following command:

pip install git+https://github.com/KRLabsOrg/RAGFactChecker.git@main

Usage

Below is a sample that shows how to import and use the LLMTripletValidator class to execute fact checking. The inputs are:

• input_text: The answer from the LLM
• reference_text: The reference documents which were fed to the LLM from the RAG system

import os
from rag_fact_checker import LLMTripletValidator
api_key = os.getenv("OPENAI_API_KEY", "your_openai_api_key")

triplet_validator = LLMTripletValidator(
  input_config = {"triplet_generator": "llm_n_shot", "fact_checker": "llm_n_shot"},
  openai_api_key=api_key
)

results = triplet_validator.validate_llm_triplets(
   input_text="The sky is green", 
   reference_text=["The sky is blue and the grass is green"]
)

Output:

DirectTextMatchOutput(
    input_triplets=[['The sky', 'is', 'green']], 
    reference_triplets=[['The sky', 'is', 'blue'], ['The grass', 'is', 'green']], 
    fact_check_prediction_binary={0: False} # this means that input triplet with idx 0 is incorrect
)

Additional usages

Besides being used for fact-checking purposes only, the library can also be used for Information Extraction purposes - namely, given some text, to extract the triplets from it (all using LLMs).

import os
from rag_fact_checker import LLMTripletValidator
api_key = os.getenv("OPENAI_API_KEY", "your_openai_api_key")


triplet_validator = LLMTripletValidator(
    input_config = {"triplet_generator": "llm_n_shot", "fact_checker": "llm_n_shot"},
    openai_api_key=api_key
)

results = triplet_validator.triplet_generation(
    input_text="The sky is green and the Eiffel Tower is blue. The Eiffel Tower is in Paris."
)

Another usecase is to use it for synthetic hallucinated data generation.

import os
from rag_fact_checker import LLMTripletValidator
api_key = os.getenv("OPENAI_API_KEY", "your_openai_api_key")

triplet_validator = LLMTripletValidator(
    input_config = {"triplet_generator": "llm_n_shot", "fact_checker": "llm_n_shot"},
    openai_api_key=api_key
)

results = triplet_validator.generate_hlcntn_data(
    question="Which genes does thyroid hormone receptor beta1 regulate in the liver?",
    reference_text=["The carbohydrate response element-binding protein (ChREBP) and sterol response element-binding protein (SREBP)-1c, regulated by liver X receptors (LXRs), play central roles in hepatic lipogenesis. Because LXRs and thyroid hormone receptors (TRs) influence each other’s transcriptional activity, researchers investigated whether TRs control ChREBP expression. They found that thyroid hormone (T3) and TR-beta1 upregulate ChREBP by binding direct repeat-4 elements (LXRE1/2), thereby fine-tuning hepatic lipid metabolism."]
)

Configuration

When creating the LLMTripletValidator, you can pass a dict to input_config to override default settings. For example, to customize model names, logging level, etc.:

custom_config = {
    "model": {
        "triplet_generator": {
            "model_name": "llm_n_shot"
        },
        "fact_checker": {
            "model_name": "llm"
        }
    },
    "logger_level": "DEBUG"
}

Available Models

• Triplet generator : "llm", "llm_n_shot"
• Fact checker : "llm", "llm_split", "llm_n_shot", "llm_n_shot_split"
• Logger level : "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL", "NOTSET"