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.
To install latest from main brach, clone the repo and conduct installation using pip, it is recommended to use virtual environment. Code example is presented below:
git clone https://github.com/IINemo/lm-polygraph.git
python3 -m venv env # Substitute this with your virtual environment creation command
source env/bin/activate
cd lm-polygraph
pip install .Installation from GitHub is recommended if you want to explore notebooks with examples or use default benchmarking configurations, as they are included in the repository but not in the PyPI package. However code from the main branch may be unstable, so it is recommended to checkout to the latest stable release before installation:
git clone https://github.com/IINemo/lm-polygraph.git
git checkout tags/v0.3.0
python3 -m venv env # Substitute this with your virtual environment creation command
source env/bin/activate
cd lm-polygraph
pip install .To install the latest stable version from PyPI, run:
python3 -m venv env # Substitute this with your virtual environment creation command
source env/bin/activate
pip install lm-polygraphTo install a specific version, run:
python3 -m venv env # Substitute this with your virtual environment creation command
source env/bin/activate
pip install lm-polygraph==0.3.0- 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. For example, with bigscience/bloomz-560m:
from transformers import AutoModelForCausalLM, AutoTokenizer
from lm_polygraph.utils.model import WhiteboxModel
model_path = "bigscience/bloomz-560m"
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)Alternatively, you can use WhiteboxModel#from_pretrained method to let LM-Polygraph download the model and tokenizer for you. However, this approach is deprecated and will be removed in the next major release.
from lm_polygraph.utils.model import WhiteboxModel
model = WhiteboxModel.from_pretrained(
"bigscience/bloomz-3b",
device_map="cuda:0",
)- Specify UE method:
from lm_polygraph.estimators import *
ue_method = MeanPointwiseMutualInformation()- Get predictions and their uncertainty scores:
from lm_polygraph.utils.manager 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='bigscience/bloomz-560m')- example.ipynb: simple examples of scoring individual queries;
- claim_level_example.ipynb: an example of scoring individual claims;
- qa_example.ipynb: an example of scoring the
bigscience/bloomz-3bmodel on theTriviaQAdataset; - mt_example.ipynb: an of scoring the
facebook/wmt19-en-demodel on theWMT14 En-Dedataset; - ats_example.ipynb: an example of scoring the
facebook/bart-large-cnnmodel on theXSUMsummarization dataset; - colab: demo web application in Colab (
bloomz-560m,gpt-3.5-turbo, andgpt-4fit the default memory limit; other models require Colab-pro).
| 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 |
| 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 |
| TokenSAR (Duan et al., 2023) | White-box | Meaning diversity | High | Low | No | sequence |
| 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 |
| 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 |
| 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 |
To evaluate the performance of uncertainty estimation methods consider a quick example:
HYDRA_CONFIG=../examples/configs/polygraph_eval_coqa.yaml python ./scripts/polygraph_eval \
dataset="coqa" \
model.path="databricks/dolly-v2-3b" \
save_path="./workdir/output" \
"seed=[1,2,3,4,5]"
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.
docker run -p 3001:3001 -it \
-v $HOME/.cache/huggingface/hub:/root/.cache/huggingface/hub \
--gpus all mephodybro/polygraph_demo:0.0.17 polygraph_serverThe server should be available on http://localhost:3001
A more detailed description of the demo is available in the documentation.
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.",
}
Submitted:
@article{vashurin2024benchmarking,
title={Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph},
author={
Vashurin, Roman and
Fadeeva, Ekaterina and
Vazhentsev, Artem and
Rvanova, Lyudmila and
Tsvigun, Akim and
Vasilev, Daniil and
Xing, Rui and
Sadallah, Abdelrahman Boda and
Grishchenkov, Kirill and
Petrakov, Sergey and
Panchenko, Alexander and
Baldwin, Timothy and
Nakov, Preslav and
Panov, Maxim and
Shelmanov, Artem},
journal={arXiv preprint arXiv:2406.15627},
year={2024}
}
The chat GUI implementation is based on the chatgpt-web-application project.