arXiv AI
Jul 16
Data-Efficient Adaptation of LLMs via Attention Head Reweighting
arXiv:2607. 13425v1 Announce Type: cross Abstract: Learning effectively from limited data is critical in domains like security where labeled examples are scarce.
High-Dimensional Theory of LoRA Fine-Tuning in a Solvable Attention Model
arXiv:2606. 05899v1 Announce Type: new Abstract: We develop a high-dimensional statistical theory of low-rank adaptation (LoRA) in attention models, capturing the interplay between pre-training and fine-tuning.
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arXiv AI
Jun 8
Limitations of Normalization in Attention Mechanism
arXiv:2508. 17821v3 Announce Type: replace-cross Abstract: This paper investigates the limitations of the normalization in attention mechanisms.
arXiv AI
Jun 18
Dual Dimensionality for Local and Global Attention
arXiv:2606. 18587v1 Announce Type: cross Abstract: Decoder-only Transformers compute attention over the KV cache of preceding tokens.
arXiv Machine Learning
Jul 30
Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training
arXiv:2505. 15548v2 Announce Type: replace Abstract: Autoregressive transformer language models frequently exhibit training instability when trained on long sequences, particularly under low-precision arithmetic.
arXiv Machine Learning
Jun 4
Customizing the Inductive Biases of Softmax Attention using Structured Matrices
arXiv:2509. 07963v2 Announce Type: replace Abstract: The core component of attention is the scoring function, which transforms the inputs into low-dimensional queries and keys and takes the dot product of each pair.
arXiv Machine Learning
Jun 5
Specialization of softmax attention heads: insights from the high-dimensional single-location model
arXiv:2603. 03993v2 Announce Type: replace Abstract: Multi-head attention enables transformer models to represent multiple attention patterns simultaneously.
arXiv Machine Learning
Jul 22
Relative Positions Generalize, Absolute Positions Memorize: An Implicit-Bias Account of Length Generalization in Attention
arXiv:2607. 18759v1 Announce Type: new Abstract: Transformers with relative positional encodings often extrapolate to sequences longer than those seen during training, whereas transformers with learned absolute encodings typically do not.
arXiv AI
Jun 16
SDS-LoRA: Overcoming Anisotropic Gradient Scaling in Low-Rank Adaptation
arXiv:2606. 16454v1 Announce Type: cross Abstract: Low-Rank Adaptation (LoRA) enables efficient adaptation of large pre-trained models to downstream tasks by parameterizing weight updates with low-rank matrices.
Hugging Face Trending Papers
Jul 22
Statistical Inference for Rank Allocation in Low-Rank Adaptation
Low-rank adaptation (LoRA) has become a widely used parameter-efficient fine-tuning method for large language models. Since different modules and layers may contribute unequally to downstream adaptation, allocating rank resources under a fixed parameter budget is an important problem for balancing efficiency, expressiveness, and generalization.
arXiv AI
Jun 26
SSM Adapters via Hankel Reduced-order Modeling: Injection Site Determines Task Suitability in Long-Context Fine-Tuning
arXiv:2606. 26290v1 Announce Type: cross Abstract: While parameter-efficient fine-tuning (PEFT) typically targets attention projectors, its efficacy for tasks requiring sequential state accumulation remains under-explored.
arXiv AI
Jun 3
Distill-then-Replace: Efficient Task-Specific Hybrid Attention Model Construction
arXiv:2601. 11667v2 Announce Type: replace-cross Abstract: Transformer architectures deliver state-of-the-art accuracy via dense full-attention, but their quadratic time and memory complexity with respect to sequence length limits practical deployment.
arXiv Machine Learning
Jun 8
TabSwift: An Efficient Tabular Foundation Model with Row-Wise Attention
arXiv:2606. 07345v1 Announce Type: new Abstract: Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examples.