Towards Improved Sentence Representations using Token Graphs
Obtaining a single-vector representation from a Large Language Model's (LLM) token-level outputs is a critical step for nearly all sentence-level tasks. However, standard pooling methods like mean or max aggregation treat tokens as an independent set, discarding the rich relational structure captured by the model's self-attention layers and making them susceptible to signal dilution. To address this, we introduce GLOT, a lightweight, structure-aware pooling module that reframes pooling as relational learning followed by aggregation. Operating on the outputs of a frozen LLM, GLOT first constructs a latent token-similarity graph, then refines token representations with a graph neural networ...
| Authors | Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Zorah Lähner et al. |
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| Primary category | cs.LG |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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