README.md

April 9, 2026 · View on GitHub

LM-Polygraph: Uncertainty Estimation for LLMs

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

LM-Polygraph is also one of the most widely used benchmarks for the consistent evaluation of uncertainty estimation and hallucination detection methods. It is adopted by hundreds of researchers and technology companies.

Installation

From GitHub

The latest stable version is available in the main branch, it is recommended to use a virtual environment:

python -m venv env # Substitute this with your virtual environment creation command
source env/bin/activate
pip install git+https://github.com/IINemo/lm-polygraph.git

You can also use tags:

pip install git+https://github.com/IINemo/lm-polygraph.git@v0.5.0

From PyPI

The latest tagged version is also available via PyPI:

pip install lm-polygraph

Optional dependencies

Some features require additional packages that are not installed by default:

COMET metric (translation evaluation): unbabel-comet pins numpy<2.0 which may conflict with packages like vLLM. Install via extras:
```
pip install lm-polygraph[comet]
```
If you need numpy 2.x (e.g., for vLLM), install without the extra and add comet manually:
```
pip install lm-polygraph
pip install unbabel-comet --no-deps
```

Basic usage

Initialize the base model (encoder-decoder or decoder-only) and tokenizer from HuggingFace or a local file, and use them to initialize the WhiteboxModel for evaluation:

from transformers import AutoModelForCausalLM, AutoTokenizer
from lm_polygraph.utils.model import WhiteboxModel

model_path = "Qwen/Qwen2.5-0.5B-Instruct"
base_model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cuda:0")
tokenizer = AutoTokenizer.from_pretrained(model_path)

model = WhiteboxModel(base_model, tokenizer, model_path=model_path)

Specify the UE method:

from lm_polygraph.estimators import *

ue_method = MeanTokenEntropy()

Get predictions and their uncertainty scores:

from lm_polygraph.utils import estimate_uncertainty

input_text = "Who is George Bush?"
ue = estimate_uncertainty(model, ue_method, input_text=input_text)
print(ue)
# UncertaintyOutput(uncertainty=-6.504108926902215, input_text='Who is George Bush?', generation_text=' President of the United States', model_path='Qwen/Qwen2.5-0.5B-Instruct')

More examples: basic_example.ipynb
See also a low-level example for efficient integration into your code: low_level_example.ipynb

Using with LLMs deployed as a service

LM-Polygraph can work with any OpenAI-compatible API services:

from lm_polygraph import BlackboxModel
from lm_polygraph.estimators import Perplexity, MaximumSequenceProbability

model = BlackboxModel.from_openai(
    openai_api_key='YOUR_API_KEY',
    model_path='gpt-4o',
    supports_logprobs=True  # Enable for deployments
)

ue_method = Perplexity()  # or MeanTokenEntropy(), EigValLaplacian(), etc.
estimate_uncertainty(model, ue_method, input_text='What has a head and a tail but no body?')

UE methods such as EigValLaplacian() support fully blackbox LLMs that do not provide logits.

More examples:

basic_example.ipynb: simple examples of scoring individual queries
low_level_example.ipynb: low-level integration into inference and claim-level UE
low_level_vllm_example.ipynb: low-level example using vLLM for faster inference
basic_visual_llm_example.ipynb: examples for visual LLMs

Overview of methods

Uncertainty Estimation Method	Type	Category	Compute	Memory	Need Training Data?	Level
Maximum sequence probability	White-box	Information-based	Low	Low	No	sequence/claim
Perplexity (Fomicheva et al., 2020a)	White-box	Information-based	Low	Low	No	sequence/claim
Mean/max token entropy (Fomicheva et al., 2020a)	White-box	Information-based	Low	Low	No	sequence/claim
Monte Carlo sequence entropy (Kuhn et al., 2023)	White-box	Information-based	High	Low	No	sequence
Pointwise mutual information (PMI) (Takayama and Arase, 2019)	White-box	Information-based	Medium	Low	No	sequence/claim
Conditional PMI (van der Poel et al., 2022)	White-box	Information-based	Medium	Medium	No	sequence
Rényi divergence (Darrin et al., 2023)	White-box	Information-based	Low	Low	No	sequence
Fisher-Rao distance (Darrin et al., 2023)	White-box	Information-based	Low	Low	No	sequence
Attention Score (Sriramanan et al., 2024)	White-box	Information-based	Medium	Low	No	sequence/claim
Contextualized Sequence Likelihood (CSL) (Lin et al., 2024)	White-box	Information-based	Medium	Low	No	sequence
Recurrent Attention-based Uncertainty Quantification (RAUQ) (Vazhentsev et al., 2025)	White-box	Information-based	Low	Low	No	sequence
Focus (Zhang et al., 2023)	White-box	Information-based	Medium	Low	No	sequence/claim
BoostedProb (Dinh et al., 2025)	White-box	Information-based	Low	Low	No	sequence/claim
Semantic entropy (Kuhn et al., 2023)	White-box	Meaning diversity	High	Low	No	sequence
Claim-Conditioned Probability (Fadeeva et al., 2024)	White-box	Meaning diversity	Low	Low	No	sequence/claim
FrequencyScoring (Mohri et al., 2024)	White-box	Meaning diversity	High	Low	No	claim
TokenSAR (Duan et al., 2023)	White-box	Meaning diversity	High	Low	No	sequence/claim
SentenceSAR (Duan et al., 2023)	White-box	Meaning diversity	High	Low	No	sequence
SAR (Duan et al., 2023)	White-box	Meaning diversity	High	Low	No	sequence
SemanticDensity (Qiu et al., 2024)	White-box	Meaning diversity	High	Low	No	sequence
CoCoA (Vashurin et al., 2025)	White-box	Meaning diversity	High	Low	No	sequence
EigenScore (Chen et al., 2024)	White-box	Meaning diversity	High	Low	No	sequence
Sentence-level ensemble-based measures (Malinin and Gales, 2020)	White-box	Ensembling	High	High	Yes	sequence
Token-level ensemble-based measures (Malinin and Gales, 2020)	White-box	Ensembling	High	High	Yes	sequence
Mahalanobis distance (MD) (Lee et al., 2018)	White-box	Density-based	Low	Low	Yes	sequence
Robust density estimation (RDE) (Yoo et al., 2022)	White-box	Density-based	Low	Low	Yes	sequence
Relative Mahalanobis distance (RMD) (Ren et al., 2023)	White-box	Density-based	Low	Low	Yes	sequence
Hybrid Uncertainty Quantification (HUQ) (Vazhentsev et al., 2023a)	White-box	Density-based	Low	Low	Yes	sequence
p(True) (Kadavath et al., 2022)	White-box	Reflexive	Medium	Low	No	sequence/claim
Number of semantic sets (NumSets) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No	sequence
Sum of eigenvalues of the graph Laplacian (EigV) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No	sequence
Degree matrix (Deg) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No	sequence
Eccentricity (Ecc) (Lin et al., 2023)	Black-box	Meaning Diversity	High	Low	No	sequence
Lexical similarity (LexSim) (Fomicheva et al., 2020a)	Black-box	Meaning Diversity	High	Low	No	sequence
Kernel Language Entropy (Nikitin et al., 2024)	Black-box	Meaning Diversity	High	Low	No	sequence
LUQ (Zhang et al., 2024)	Black-box	Meaning diversity	High	Low	No	sequence
Verbalized Uncertainty 1S (Tian et al., 2023)	Black-box	Reflexive	Low	Low	No	sequence
Verbalized Uncertainty 2S (Tian et al., 2023)	Black-box	Reflexive	Medium	Low	No	sequence

Benchmark

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

CUDA_VISIBLE_DEVICES=0 polygraph_eval \
    --config-dir=./examples/configs/ \
    --config-name=polygraph_eval_coqa.yaml \
    model.path=meta-llama/Llama-3.1-8B \
    subsample_eval_dataset=100

To evaluate the performance of uncertainty estimation methods using vLLM for generation, consider the following example:

CUDA_VISIBLE_DEVICES=0 polygraph_eval \
    --config-dir=./examples/configs/ \
    --config-name=polygraph_eval_coqa.yaml \
    model=vllm \
    model.path=meta-llama/Llama-3.1-8B \
    estimators=default_estimators_vllm \
    stat_calculators=default_calculators_vllm \
    subsample_eval_dataset=100

You can also use a pre-built docker container for benchmarking, example:

docker run --gpus '"device=0"' --rm \
  -w /app \
  inemo/lm_polygraph \
  bash -c "polygraph_eval \
    --config-dir=./examples/configs/ \
    --config-name=polygraph_eval_coqa.yaml \
    model.path=meta-llama/Llama-3.1-8B \
    subsample_eval_dataset=100"

The benchmark datasets in the correct format could be found in the HF repo. The scripts for dataset preparation could be found in the dataset_builders directory.

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

A detailed description of the benchmark is in the documentation.

(Obsolete) Demo web application

Currently unsupported.

Cite

TACL-2025 paper:

@article{shelmanovvashurin2025,
    author = {Vashurin, Roman and Fadeeva, Ekaterina and Vazhentsev, Artem and Rvanova, Lyudmila and Vasilev, Daniil and Tsvigun, Akim and Petrakov, Sergey and Xing, Rui and Sadallah, Abdelrahman and Grishchenkov, Kirill and Panchenko, Alexander and Baldwin, Timothy and Nakov, Preslav and Panov, Maxim and Shelmanov, Artem},
    title = {Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {13},
    pages = {220-248},
    year = {2025},
    month = {03},
    issn = {2307-387X},
    doi = {10.1162/tacl_a_00737},
    url = {https://doi.org/10.1162/tacl\_a\_00737},
    eprint = {https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl\_a\_00737/2511955/tacl\_a\_00737.pdf},
}

ACL-2025 Tutorial:

@inproceedings{shelmanov-etal-2025-uncertainty,
    title = "Uncertainty Quantification for Large Language Models",
    author = "Shelmanov, Artem  and
      Panov, Maxim  and
      Vashurin, Roman  and
      Vazhentsev, Artem  and
      Fadeeva, Ekaterina  and
      Baldwin, Timothy",
    editor = "Arase, Yuki  and
      Jurgens, David  and
      Xia, Fei",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2025.acl-tutorials.3/",
    doi = "10.18653/v1/2025.acl-tutorials.3",
    pages = "3--4",
    ISBN = "979-8-89176-255-8"
}

EMNLP-2023 paper:

@inproceedings{fadeeva-etal-2023-lm,
    title = "{LM}-Polygraph: Uncertainty Estimation for Language Models",
    author = "Fadeeva, Ekaterina  and
      Vashurin, Roman  and
      Tsvigun, Akim  and
      Vazhentsev, Artem  and
      Petrakov, Sergey  and
      Fedyanin, Kirill  and
      Vasilev, Daniil  and
      Goncharova, Elizaveta  and
      Panchenko, Alexander  and
      Panov, Maxim  and
      Baldwin, Timothy  and
      Shelmanov, Artem",
    editor = "Feng, Yansong  and
      Lefever, Els",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.emnlp-demo.41",
    doi = "10.18653/v1/2023.emnlp-demo.41",
    pages = "446--461",
    abstract = "Recent advancements in the capabilities of large language models (LLMs) have paved the way for a myriad of groundbreaking applications in various fields. However, a significant challenge arises as these models often {``}hallucinate{''}, i.e., fabricate facts without providing users an apparent means to discern the veracity of their statements. Uncertainty estimation (UE) methods are one path to safer, more responsible, and more effective use of LLMs. However, to date, research on UE methods for LLMs has been focused primarily on theoretical rather than engineering contributions. In this work, we tackle this issue by introducing LM-Polygraph, a framework with implementations of a battery of state-of-the-art UE methods for LLMs in text generation tasks, with unified program interfaces in Python. Additionally, it 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. LM-Polygraph is compatible with the most recent LLMs, including BLOOMz, LLaMA-2, ChatGPT, and GPT-4, and is designed to support future releases of similarly-styled LMs.",
}

Acknowledgements

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