lm-polygraph 0.4.0

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

From GitHub

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 .

From PyPI

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-polygraph

To 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

Basic usage

1. 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",
)

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?"
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')

Other examples:

• 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-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?	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

Benchmark

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.

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.

Cite

@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.