[Submitted on 7 Oct 2026]
Title:Lossy Compressive Text Autoencoders
View a PDF of the paper titled Lossy Compressive Text Autoencoders, by Vinko Sabol\v{c}ec and 2 other authors
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Abstract:Our work explores learning a compressed latent representation of text, at the intersection of data compression and representation learning. We propose an autoencoder architecture that performs residual downscaling and upscaling of hidden representations along the time axis, with a residual low-dimension discrete bottleneck. We analyze our approach for different quantization methods, training objectives, and datasets. For different levels of compression, we evaluate the similarity between the original and reconstructed text both at the surface-level (BLEU) and at the semantic-level (LLM-based judge). Additionally, we evaluate our models on downstream question-answering and semantic text similarity benchmarks. Our approach results in compressed representations which are on par with lossless text compression algorithms at 2.24 bits per byte on web text data, while having good reconstruction and downstream task performance.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2610.10738 [cs.CL]
(or arXiv:2610.10738v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.10738
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: David Grangier [view email] [v1] Wed, 7 Oct 2026 18:08:40 UTC (14,137 KB)
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