aiqbench analysis
This paper is squarely an ML research contribution: a parameter-efficient fine-tuning method (SWIFT) for large language models that uses block-diagonal sparse updates paired with a fixed scattering permutation, borrowing the small-world idea to avoid "information islands" in sparse adaptation. The practical claim is that this structure can match or beat other PEFT baselines and sometimes full fine-tuning, with no extra trainable parameters, but the evidence is benchmark-based on commonsense reasoning, generation, and image classification, so readers should check reproducibility and compute trade-offs against LoRA-style alternatives. The theoretical expansion guarantees are the more reusable takeaway for anyone designing sparse adapters.
Breaking Information Islands in Sparse Tuning via Small-World Connectivity
arXiv:2610.41665v1 Announce Type: new Abstract: Sparse tuning is widely used to adapt large language models due to its parameter efficiency. However, its effectiveness depends not only on the number of trainable parameters, but also on the feature-interaction topology of the sparse update's structural support. Limited or uneven connectivity in this support may isolate subsets of feature dimensions, forming information islands that restrict direct task-specific interactions through the trainable update. To address this issue, we propose Small-World Induced Fine-Tuning (SWIFT), a sparse tuning framework inspired by small-world connectivity. SWIFT combines block-diagonal sparse updates for local feature mixing with a fixed deterministic scattering permutation that routes the mixed outputs across feature groups without additional trainable parameters. We further show theoretically that certain random sparse-update models and block-diagonal updates can exhibit information islands, whereas a sufficiently scattering permutation guarantees positive block-level expansion when the trainable block-diagonal factor consists of equal-sized, structurally dense square blocks. Extensive experiments on commonsense reasoning, natural language generation, and image classification demonstrate that SWIFT outperforms the evaluated parameter-efficient fine-tuning (PEFT) baselines on average and can match or surpass full fine-tuning. Implementation is available at https://github.com/xianchaoguan/SWIFT
Abstract:Sparse tuning is widely used to adapt large language models due to its parameter efficiency. However, its effectiveness depends not only on the number of trainable parameters, but also on the feature-interaction topology of the sparse update's structural support. Limited or uneven connectivity in this support may isolate subsets of feature dimensions, forming information islands that restrict direct task-specific interactions through the trainable update. To address this issue, we propose Small-World Induced Fine-Tuning (SWIFT), a sparse tuning framework inspired by small-world connectivity. SWIFT combines block-diagonal sparse updates for local feature mixing with a fixed deterministic scattering permutation that routes the mixed outputs across feature groups without additional trainable parameters. We further show theoretically that certain random sparse-update models and block-diagonal updates can exhibit information islands, whereas a sufficiently scattering permutation guarantees positive block-level expansion when the trainable block-diagonal factor consists of equal-sized, structurally dense square blocks. Extensive experiments on commonsense reasoning, natural language generation, and image classification demonstrate that SWIFT outperforms the evaluated parameter-efficient fine-tuning (PEFT) baselines on average and can match or surpass full fine-tuning. Implementation is available at this https URL
Accepted at NeurIPS 2026. 23 pages, 6 figures
Artificial Intelligence (cs.AI)
arXiv:2610.41665 [cs.AI]
(or arXiv:2610.41665v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.41665 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 285 words
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