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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Edit Distance Embedding with Genomic Large Language Model.

Xiang Li¹, Ke Chen¹, Yijia Zhang²

¹Department of Computer Science and Engineering, The Pennsylvania State University.

Biorxiv : the Preprint Server for Biology

|November 19, 2025

Summary

This summary is machine-generated.

LLMED, a new model leveraging DNA language models, enhances genomic sequence analysis by approximating edit distance. This approach improves upon existing machine learning methods for tasks like similar sequence search.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Edit distance is crucial for genomic sequence analysis but computationally intensive.
Current edit distance embedding methods, including machine learning approaches, have limitations in accuracy.
Genomic language models show promise for various sequence analysis tasks.

Purpose of the Study:

To investigate the use of DNA language models for improved edit distance embedding.
To introduce LLMED, a novel model for approximating edit distance through sequence embeddings.
To evaluate LLMED's performance against existing embedding techniques.

Main Methods:

LLMED is trained using contrastive learning.
The model utilizes a pretrained genomic large language model.
Embeddings are generated to approximate edit distance in a normed space.

Main Results:

LLMED demonstrates superior performance in approximating edit distance compared to leading machine learning and rule-based methods.
LLMED significantly improves accuracy in similar sequence search applications.
Experimental comparisons validate LLMED's effectiveness.

Conclusions:

DNA language models can be effectively utilized for accurate edit distance approximation.
LLMED offers a promising advancement in genomic sequence analysis and similarity searching.
The LLMED approach addresses limitations of current embedding methods.