Papers with nonlinear
Geometric Signatures of Compositionality Across a Language Model’s Lifetime (2025.acl-long)
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| Challenge: | linguistic compositionality allows atoms to locally combine to create global meaning . a rich array of meanings at the level of a phrase may be explained by simple rules of composition. |
| Approach: | They propose to relate the degree of compositionality in a dataset to the intrinsic dimension of its representations under an LM, a measure of feature complexity. |
| Outcome: | The proposed model is based on a geometric view of the compositionality of a dataset and the intrinsic dimension of its representations under an LM. |
ResFormer: All-Time Reservoir Memory for Long Sequence Classification (2025.emnlp-main)
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| Challenge: | Existing models with quadratic time and memory complexity restrict input length . however, analyzing extensive sequential contexts is challenging . |
| Approach: | They propose a neural network architecture that captures contextual dependencies in linear time and a nonlinear readout to model short-term dependencies within sentences. |
| Outcome: | The proposed model outperforms baseline models on EmoryNLP datasets and on IEMOCAP and MultiWOZ datasets. |
Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions (2026.findings-acl)
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| Challenge: | Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models, but its strictly linear structure limits expressive capacity. |
| Approach: | They propose a method that introduces structured polynomial expansion directly into the low-rank factor space. |
| Outcome: | The proposed method outperforms state-of-the-art methods across diverse benchmarks. |
Lˆ2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification (2024.lrec-main)
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| Challenge: | Existing linear GCNs perform neural network operations in Euclidean space, which do not capture tree-like hierarchical structure of graphs. |
| Approach: | They propose a Lorentzian linear GCN framework that maps features into hyperbolic space and performs a feature transformation to capture the underlying tree-like structure of data. |
| Outcome: | The proposed framework achieves state-of-the-art accuracy on standard citation networks datasets and 81.3% on PubMed datasets. |