@inproceedings{zhang-bollegala-2026-map,
title = "Map of Encoders {--} Mapping Sentence Encoders using Quantum Relative Entropy",
author = "Zhang, Gaifan and
Bollegala, Danushka",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.645/",
pages = "14160--14208",
ISBN = "979-8-89176-390-6",
abstract = "We propose a method to compare and visualise sentence encoders at scale by creating a map of encoders where each sentence encoder is represented in relation to the other sentence encoders. Specifically, we first represent a sentence encoder using an embedding matrix of a sentence set, where each row corresponds to the embedding of a sentence. Next, we compute the PIP matrix for a sentence encoder using its embedding matrix. Finally, we create a feature vector for each sentence encoder that reflects its QRE with respect to a unit base encoder. We construct a map of encoders covering 1101 publicly available sentence encoders, providing a new perspective of the landscape of the pre-trained sentence encoders. Our map accurately reflects various relationships between encoders, where encoders with similar attributes are proximally located on the map. Moreover, our encoder feature vectors can be used to accurately infer downstream task performance of the encoders, such as in retrieval and clustering tasks, demonstrating the correctness of our map."
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<abstract>We propose a method to compare and visualise sentence encoders at scale by creating a map of encoders where each sentence encoder is represented in relation to the other sentence encoders. Specifically, we first represent a sentence encoder using an embedding matrix of a sentence set, where each row corresponds to the embedding of a sentence. Next, we compute the PIP matrix for a sentence encoder using its embedding matrix. Finally, we create a feature vector for each sentence encoder that reflects its QRE with respect to a unit base encoder. We construct a map of encoders covering 1101 publicly available sentence encoders, providing a new perspective of the landscape of the pre-trained sentence encoders. Our map accurately reflects various relationships between encoders, where encoders with similar attributes are proximally located on the map. Moreover, our encoder feature vectors can be used to accurately infer downstream task performance of the encoders, such as in retrieval and clustering tasks, demonstrating the correctness of our map.</abstract>
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%0 Conference Proceedings
%T Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy
%A Zhang, Gaifan
%A Bollegala, Danushka
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F zhang-bollegala-2026-map
%X We propose a method to compare and visualise sentence encoders at scale by creating a map of encoders where each sentence encoder is represented in relation to the other sentence encoders. Specifically, we first represent a sentence encoder using an embedding matrix of a sentence set, where each row corresponds to the embedding of a sentence. Next, we compute the PIP matrix for a sentence encoder using its embedding matrix. Finally, we create a feature vector for each sentence encoder that reflects its QRE with respect to a unit base encoder. We construct a map of encoders covering 1101 publicly available sentence encoders, providing a new perspective of the landscape of the pre-trained sentence encoders. Our map accurately reflects various relationships between encoders, where encoders with similar attributes are proximally located on the map. Moreover, our encoder feature vectors can be used to accurately infer downstream task performance of the encoders, such as in retrieval and clustering tasks, demonstrating the correctness of our map.
%U https://aclanthology.org/2026.acl-long.645/
%P 14160-14208
Markdown (Informal)
[Map of Encoders – Mapping Sentence Encoders using Quantum Relative Entropy](https://aclanthology.org/2026.acl-long.645/) (Zhang & Bollegala, ACL 2026)
ACL