aiqbench analysis
This research demonstrates that relying solely on the final layers of protein language models is often suboptimal, as intermediate layers frequently yield superior embeddings for specific downstream tasks. The authors link this performance variation to the alignment between task objectives and pre-training goals, offering a tool to automatically select efficient model truncations. This finding provides a concrete method for optimizing inference speed and memory usage without sacrificing accuracy in computational biology applications.
Task- and dataset-specific information in protein language models
arXiv:2608.12090v3 Announce Type: replace Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs). By consensus, embeddings from the models' last layers are used, while the models' internal behavior remains poorly understood. We analyzed 13 PLMs across 15 DTs and 9 datasets to assess the value of embeddings from intermediate PLM layers. We trained probe models on embeddings from each layer, compared their performance, and showed that the last layers of PLMs rarely produced embeddings that led to the best results on downstream tasks. Furthermore, we identified a connection between how models learn a certain DT and the similarity between that DT and the pre-training objective. For example, for residue-level downstream tasks, we observed a steady increase in performance across almost all PLM layers, which we attributed to their similarity to most PLMs' pre-training objectives. To allow the community to capitalize on our findings, we provide PLMSommelier, a Python package that automatically identifies the best PLM layer for a given DT with ~98% accuracy and creates a truncated model using only the early layers up to the best-performing layer. This will help users save time and memory during inference and yield better predictive performance.
Abstract:Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology. These models, trained on large corpora of protein sequence data, are widely used to translate amino acid sequences into latent-space embeddings, ready for use in diverse downstream tasks (DTs). By consensus, embeddings from the models' last layers are used, while the models' internal behavior remains poorly understood. We analyzed 13 PLMs across 15 DTs and 9 datasets to assess the value of embeddings from intermediate PLM layers. We trained probe models on embeddings from each layer, compared their performance, and showed that the last layers of PLMs rarely produced embeddings that led to the best results on downstream tasks. Furthermore, we identified a connection between how models learn a certain DT and the similarity between that DT and the pre-training objective. For example, for residue-level downstream tasks, we observed a steady increase in performance across almost all PLM layers, which we attributed to their similarity to most PLMs' pre-training objectives. To allow the community to capitalize on our findings, we provide PLMSommelier, a Python package that automatically identifies the best PLM layer for a given DT with ~98% accuracy and creates a truncated model using only the early layers up to the best-performing layer. This will help users save time and memory during inference and yield better predictive performance.
Machine Learning (cs.LG); Biomolecules (q-bio.BM)
arXiv:2608.12090 [cs.LG]
(or arXiv:2608.12090v3 [cs.LG] for this version)
From: Roman Joeres Mr. [view email] [v1] Wed, 12 Aug 2026 14:13:48 UTC (822 KB) [v2] Thu, 13 Aug 2026 07:47:02 UTC (822 KB) [v3] Mon, 14 Sep 2026 21:05:27 UTC (5,486 KB)
Full text under the publisher's open licence · 271 words
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