@inproceedings{tang-etal-2026-gmsa,
title = "{GMSA}: Enhancing Context Compression via Group Merging and Layer Semantic Alignment",
author = "Tang, Jiwei and
Zhang, Zhicheng and
Wu, Shunlong and
Ye, Jingheng and
Bai, Lichen and
Wang, Zitai and
Lu, Tingwei and
Hai, Lin and
Zhao, Yiming and
Zheng, Hai-Tao and
Kim, Hong-Gee",
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.1324/",
pages = "28690--28704",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models (LLMs) have achieved remarkable performance across a wide range of Natural Language Processing (NLP) tasks. However, in long-context scenarios, they face two challenges: high computational cost and information redundancy. To address these challenges, we propose GMSA, an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. GMSA introduces Group Merging to achieve more uniform aggregation, mitigating semantic dominance during autoencoder pretraining, and Layer Semantic Alignment (LSA) to bridge the semantic gap between high-level abstract semantics and low-level input semantics. We first pretrain GMSA as an autoencoder and then fine-tune it for downstream tasks. Experiments demonstrate that GMSA improves context reconstruction compared to existing soft prompt compression paradigm and outperforms baselines on multiple long-context question answering and summarization benchmarks across two backbone models, while maintaining low end-to-end latency."
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%0 Conference Proceedings
%T GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment
%A Tang, Jiwei
%A Zhang, Zhicheng
%A Wu, Shunlong
%A Ye, Jingheng
%A Bai, Lichen
%A Wang, Zitai
%A Lu, Tingwei
%A Hai, Lin
%A Zhao, Yiming
%A Zheng, Hai-Tao
%A Kim, Hong-Gee
%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 tang-etal-2026-gmsa
%X Large Language Models (LLMs) have achieved remarkable performance across a wide range of Natural Language Processing (NLP) tasks. However, in long-context scenarios, they face two challenges: high computational cost and information redundancy. To address these challenges, we propose GMSA, an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks. GMSA introduces Group Merging to achieve more uniform aggregation, mitigating semantic dominance during autoencoder pretraining, and Layer Semantic Alignment (LSA) to bridge the semantic gap between high-level abstract semantics and low-level input semantics. We first pretrain GMSA as an autoencoder and then fine-tune it for downstream tasks. Experiments demonstrate that GMSA improves context reconstruction compared to existing soft prompt compression paradigm and outperforms baselines on multiple long-context question answering and summarization benchmarks across two backbone models, while maintaining low end-to-end latency.
%U https://aclanthology.org/2026.acl-long.1324/
%P 28690-28704
Markdown (Informal)
[GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment](https://aclanthology.org/2026.acl-long.1324/) (Tang et al., ACL 2026)
ACL
- Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, and Hong-Gee Kim. 2026. GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28690–28704, San Diego, California, United States. Association for Computational Linguistics.