Quickstart Guide

Create a virtual environment for using uqlm

We recommend creating a new virtual environment using venv before installing the package. To do so, please follow instructions here.

Installation

Install using pip directly from the GitHub repository.

pip install uqlm

Usage

Below are minimal examples for hallucination detection.

Example 1: Black-Box Scorers for hallucination detection

These scorers assess uncertainty by measuring the consistency of multiple responses generated from the same prompt. They are compatible with any LLM, intuitive to use, and don’t require access to internal model states or token probabilities.

Below is a sample of code illustrating how to use the BlackBoxUQ class to conduct hallucination detection.

from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model='gemini-pro')

from uqlm import BlackBoxUQ
bbuq = BlackBoxUQ(llm=llm, scorers=["semantic_negentropy"], use_best=True)

results = await bbuq.generate_and_score(prompts=prompts, num_responses=5)
results.to_df()

Above, use_best=True implements mitigation so that the uncertainty-minimized responses is selected. Note that although we use ChatVertexAI in this example, any LangChain Chat Model may be used. For a more detailed demo, refer to our Black-Box UQ Demo.

Example 2: White-Box Scorers for hallucination detection

These scorers leverage token probabilities to estimate uncertainty. They are significantly faster and cheaper than black-box methods, but require access to the LLM’s internal probabilities, meaning they are not necessarily compatible with all LLMs/APIs.

Below is a sample of code illustrating how to use the WhiteBoxUQ class to conduct hallucination detection.

from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model='gemini-pro')

from uqlm import WhiteBoxUQ
wbuq = WhiteBoxUQ(llm=llm, scorers=["min_probability"])

results = await wbuq.generate_and_score(prompts=prompts)
results.to_df()

Again, any LangChain Chat Model may be used in place of ChatVertexAI. For a more detailed demo, refer to our White-Box UQ Demo.

Example 3: LLM-as-a-Judge Scorers for hallucination detection

These scorers use one or more LLMs to evaluate the reliability of the original LLM’s response. They offer high customizability through prompt engineering and the choice of judge LLM(s).

Below is a sample of code illustrating how to use the LLMPanel class to conduct hallucination detection using a panel of LLM judges.

from langchain_google_vertexai import ChatVertexAI
llm1 = ChatVertexAI(model='gemini-1.0-pro')
llm2 = ChatVertexAI(model='gemini-1.5-flash-001')
llm3 = ChatVertexAI(model='gemini-1.5-pro-001')

from uqlm import LLMPanel
panel = LLMPanel(llm=llm1, judges=[llm1, llm2, llm3])

results = await panel.generate_and_score(prompts=prompts)
results.to_df()

Note that although we use ChatVertexAI in this example, we can use any LangChain Chat Model as judges. For a more detailed demo, refer to our LLM-as-a-Judge Demo.

Example 4: Ensemble Scorers for hallucination detection

These scorers leverage a weighted average of multiple individual scorers to provide a more robust uncertainty/confidence estimate. They offer high flexibility and customizability, allowing you to tailor the ensemble to specific use cases.

Below is a sample of code illustrating how to use the UQEnsemble class to conduct hallucination detection.

from langchain_google_vertexai import ChatVertexAI
llm = ChatVertexAI(model='gemini-pro')

from uqlm import UQEnsemble
## ---Option 1: Off-the-Shelf Ensemble---
# uqe = UQEnsemble(llm=llm)
# results = await uqe.generate_and_score(prompts=prompts, num_responses=5)

## ---Option 2: Tuned Ensemble---
scorers = [ # specify which scorers to include
   "exact_match", "noncontradiction", # black-box scorers
   "min_probability", # white-box scorer
   llm # use same LLM as a judge
]
uqe = UQEnsemble(llm=llm, scorers=scorers)

# Tune on tuning prompts with provided ground truth answers
tune_results = await uqe.tune(
   prompts=tuning_prompts, ground_truth_answers=ground_truth_answers
)
# ensemble is now tuned - generate responses on new prompts
results = await uqe.generate_and_score(prompts=prompts)
results.to_df()

As with the other examples, any LangChain Chat Model may be used in place of ChatVertexAI. For more detailed demos, refer to our Off-the-Shelf Ensemble Demo (quick start) or our Ensemble Tuning Demo (advanced).

Example notebooks

Refer to our example notebooks for examples illustrating how to use uqlm.