lm-polygraph 0.0.1.dev0

Uncertainty Estimation Toolkit for Transformer Language Models

LM-Polygraph: Uncertainty estimation for LLMs

Installation | Basic usage | Overview | Benchmark | Demo application | Documentation

LM-Polygraph provides a battery of state-of-the-art of uncertainty estimation (UE) methods for LMs in text generation tasks. High uncertainty can indicate the presence of hallucinations and knowing a score that estimates uncertinaty can help to make applications of LLMs safer.

The framework also introduces an extendable benchmark for consistent evaluation of UE techniques by researchers and a demo web application that enriches the standard chat dialog with confidence scores, empowering end-users to discern unreliable responses.

Installation

git clone https://github.com/IINemo/lm-polygraph.git && cd lm-polygraph && pip install .

Basic usage

1. Initialize the model (encoder-decoder or decoder-only) from HuggingFace or a local file. For example, bigscience/bloomz-3b

from lm_polygraph.utils.model import WhiteboxModel

model = WhiteboxModel.from_pretrained(
    "bigscience/bloomz-3b",
    device="cuda:0",
)

2. Specify UE method

from lm_polygraph.estimators import *

ue_method = MeanPointwiseMutualInformation()

3. Get predictions and their uncertainty scores

from lm_polygraph.utils.manager import estimate_uncertainty

input_text = "Who is George Bush?"
estimate_uncertainty(model, ue_method, input_text=input_text)

Other examples:

• example.ipynb: simple examples of scoring individual queries;
• qa_example.ipynb: an example of scoring the bigscience/bloomz-3b model on the TriviaQA dataset;
• mt_example.ipynb: an of scoring the facebook/wmt19-en-de model on the WMT14 En-De dataset;
• ats_example.ipynb: an example of scoring the facebook/bart-large-cnn model on the XSUM summarization dataset;
• colab: demo web application in Colab (bloomz-560m, gpt-3.5-turbo, and gpt-4 fit the default memory limit; other models require Colab-pro).

Overview of methods

Uncertainty Estimation Method	Type	Category	Compute	Memory	Need Training Data?
Maximum sequence probability	White-box	Information-based	Low	Low	No
Perplexity (Fomicheva et al., 2020a)	White-box	Information-based	Low	Low	No
Mean token entropy (Fomicheva et al., 2020a)	White-box	Information-based	Low	Low	No
Monte Carlo sequence entropy (Kuhn et al., 2023)	White-box	Information-based	High	Low	No
Pointwise mutual information (PMI) (Takayama and Arase, 2019)	White-box	Information-based	Medium	Low	No
Conditional PMI (van der Poel et al., 2022)	White-box	Information-based	Medium	Medium	No
Semantic entropy (Kuhn et al., 2023)	White-box	Meaning diversity	High	Low	No
Sentence-level ensemble-based measures (Malinin and Gales, 2020)	White-box	Ensembling	High	High	Yes
Token-level ensemble-based measures (Malinin and Gales, 2020)	White-box	Ensembling	High	High	Yes
Mahalanobis distance (MD) (Lee et al., 2018)	White-box	Density-based	Low	Low	Yes
Robust density estimation (RDE) (Yoo et al., 2022)	White-box	Density-based	Low	Low	Yes
Relative Mahalanobis distance (RMD) (Ren et al., 2023)	White-box	Density-based	Low	Low	Yes
Hybrid Uncertainty Quantification (HUQ) (Vazhentsev et al., 2023a)	White-box	Density-based	Low	Low	Yes
p(True) (Kadavath et al., 2022)	White-box	Reflexive	Medium	Low	No
Number of semantic sets (NumSets) (Kuhn et al., 2023)	Black-box	Meaning Diversity	High	Low	No
Sum of eigenvalues of the graph Laplacian (EigV) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No
Degree matrix (Deg) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No
Eccentricity (Ecc) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No
Lexical similarity (LexSim) (Fomicheva et al., 2020a)	Black-box	Meaning Diversity	High	Low	No

Benchmark

To evaluate the performance of uncertainty estimation methods consider a quick example:

HYDRA_CONFIG=../configs/polygraph_eval/polygraph_eval.yaml python ./scripts/polygraph_eval \
    dataset="./workdir/data/triviaqa.csv" \
    model="databricks/dolly-v2-3b" \
    save_path="./workdir/output" \
    seed=[1,2,3,4,5]

Use visualization_tables.ipynb to generate the summarizing tables for an experiment.

A detailed description of the benchmark is in the documentation.

Demo web application

Start with Docker

docker run -p 3001:3001 -it \
    -v $HOME/.cache/huggingface/hub:/root/.cache/huggingface/hub \
    --gpus all mephodybro/polygraph_demo:0.0.17 polygraph_server

The server should be available on http://localhost:3001

A more detailed description of the demo is available in the documentation.

Acknowledgements

The chat GUI implementation is based on the chatgpt-web-application project.