lm-polygraph 0.0.1.dev1

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.

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.