A key limitation of large language models (LLMs) is their inability to capture less-frequent or up-to-date entity knowledge, often leading to factual inaccuracies and hallucinations. Retrieval-augmented generation (RAG), which incorporates external knowledge through retrieval, is a common approach to mitigate this issue.

Although RAG typically relies on embedding-based retrieval, the embedding models themselves are also based on language models and therefore struggle with queries involving less-frequent entities, often failing to retrieve the crucial knowledge needed to overcome this limitation.

Knowledgeable Embedding addresses this challenge by injecting real-world entity knowledge into embeddings, making them more knowledgeable.

The entity knowledge is pluggable and can be dynamically updated.

For further details, refer to our paper on arXiv or GitHub repository.

Model List

Model	Model Size	Base Model
knowledgeable-ai/kpr-bert-base-uncased	112M	bert-base-uncased
knowledgeable-ai/kpr-retromae	112M	RetroMAE
knowledgeable-ai/kpr-bge-base-en	112M	bge-base-en
knowledgeable-ai/kpr-bge-base-en-v1.5	112M	bge-base-en-v1.5
knowledgeable-ai/kpr-bge-large-en-v1.5	340M	bge-large-en-v1.5

For practical use, we recommend knowledgeable-ai/kpr-bge-*, which significantly outperforms state-of-the-art models on queries involving less-frequent entities while performing comparably on other queries, as reported in our paper on ArXiv.

Regarding the model size, we do not count the entity embeddings since they are stored in CPU memory and have a negligible impact on runtime performance. See this page for details.

Model Details

Model Name: kpr-bge-large-en-v1.5
Base Model: bge-large-en-v1.5
Maximum Sequence Length: 512
Embedding Dimension: 1024

Usage

This model can be used via Hugging Face Transformers or Sentence Transformers:

Hugging Face Transformers

python

from transformers import AutoTokenizer, AutoModel
import torch

MODEL_NAME_OR_PATH = "knowledgeable-ai/kpr-bge-large-en-v1.5"

input_texts = [
    "Who founded Dominican Liberation Party?",
    "Who owns Mompesson House?"
]

# Load model and tokenizer from the Hugging Face Hub
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, trust_remote_code=True)
model = AutoModel.from_pretrained(MODEL_NAME_OR_PATH, trust_remote_code=True)

# Preprocess the text
preprocessed_inputs = tokenizer(input_texts, return_tensors="pt", padding=True)

# Compute embeddings
with torch.no_grad():
    embeddings = model.encode(**preprocessed_inputs)

print("Embeddings:", embeddings)

Sentence Transformers

python

from sentence_transformers import SentenceTransformer

MODEL_NAME_OR_PATH = "knowledgeable-ai/kpr-bge-large-en-v1.5"

input_texts = [
    "Who founded Dominican Liberation Party?",
    "Who owns Mompesson House?"
]

# Load model from the Hugging Face Hub
model = SentenceTransformer(MODEL_NAME_OR_PATH, trust_remote_code=True)

# Compute embeddings
embeddings = model.encode(input_texts)

print("Embeddings:", embeddings)

IMPORTANT: This code will be supported in versions of Sentence Transformers later than v5.1.0, which have not yet been released at the time of writing. Until then, please install the library directly from GitHub:

pip install git+https://github.com/UKPLab/sentence-transformers.git

License

This model is licensed under the Apache License, Version 2.0.

Citation

If you use this model in your research, please cite the following paper: Dynamic Injection of Entity Knowledge into Dense Retrievers

bibtex

@article{yamada2025kpr,
title={Dynamic Injection of Entity Knowledge into Dense Retrievers},
author={Ikuya Yamada and Ryokan Ri and Takeshi Kojima and Yusuke Iwasawa and Yutaka Matsuo},
journal={arXiv preprint arXiv:2507.03922},
year={2025}
}