Pith. sign in

REVIEW 3 major objections 5 minor 1 cited by

Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Static per-head thresholds can replace top-k search in transformer attention, cutting attention elements up to 10x and V-cache rows 3-10x with under 1% accuracy loss on LLaMA2 and LLaMA3.

desk verdict This is a genuinely useful training-free attention sparsifier with measured savings, but the VMC proof is unjustified and the k-fidelity on long sequences is unverified. read the letter →

arxiv 2502.08363 v3 pith:SX65M2EK submitted 2025-02-12 cs.CL cs.AI

Konstantin Berestizshevsky , Renzo Andri , Lukas Cavigelli This is my paper · ORCID
classification cs.CLcs.AI
keywords attentionsparsificationthreshold-basedpruningKVcachecompressiontop-ksoftmaxdenominatorcompensationV-meanLLMinferencecontent-basedsparsity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a fixed threshold, calibrated once per attention head, can substitute for the top-k search normally used to sparsify transformer attention. If true, content-based sparsity becomes a simple elementwise comparison at inference, with no retraining and no full-row dependency, which makes it compatible with tiled and distributed kernels. The paper also introduces two numerical compensations that restore the probability mass and value information lost when attention elements are pruned. The payoff is a 3-10x reduction in V-cache reads and up to 10x fewer attention elements with under 1% accuracy degradation on LLaMA2 and LLaMA3, and in some benchmarks accuracy slightly above the full-attention baseline.

What carries the argument

The load-bearing object is the calibrated threshold $\theta_{l,h,r}(k)$, one per layer, head, and attention-row position, obtained by averaging the per-sample $(n-k)/n$ quantiles of the attention row and optionally adding a standard-deviation offset. At inference this turns sparsification into an elementwise comparison, removing the full-row dependency that top-k search imposes and allowing attention tiles to be processed independently. Two compensation identities carry the accuracy restoration: softmax denominator compensation multiplies the pruned softmax output by $R/(R+E)$, where $R$ and $E$ are the exponent sums of selected and discarded scores, and V-mean compensation adds $\beta\mu$, the dropped probability mass times the running mean of the value-matrix rows. The paper also fits the per-row thresholds as a function of sequence length, compressing hundreds of parameters into a few fitted coefficients.

What would settle it

Apply thresholds calibrated on a few hundred short QA prompts to 15,000-token LongBench inputs and count the elements that pass each per-row threshold; if the average selected count exceeds $2k$ (or falls below $k/2$) while accuracy drops beyond 1%, the static-threshold claim is falsified. The same test can be run by measuring the per-row $(n-k)/n$ quantile across domains and checking stability.

Watch

Extended reading notes

Core claim

The paper's central claim is that static, per-head thresholds can be calibrated to keep approximately $k$ significant attention elements per row, making thresholding a practical and principled alternative to top-k attention. At inference, each attention score is compared against the threshold for its layer, head, and row position; scores below it are discarded, and only the surviving rows of the value matrix are loaded. The method works because each row of each attention head has a stable score distribution, so a threshold chosen as the $(n-k)/n$ quantile of calibration rows keeps about $k$ elements even when the input domain changes. To preserve accuracy under aggressive sparsification, the paper adds softmax denominator compensation, which renormalizes for discarded exponentiated scores, and V-mean compensation, which adds back the mean value row scaled by the dropped attention mass. On LLaMA2 and LLaMA3, the paper reports up to 10x fewer attention elements and 3-10x fewer V-rows with under 1% accuracy degradation across QA, code generation, and long-sequence summarization tasks.

Load-bearing premise

The claim collapses if the per-row distribution of attention scores is not stable across prompts, domains, and sequence lengths, because a threshold calibrated on a small set is then no longer guaranteed to keep about $k$ elements per row on new inputs.

Editorial extensions

If this is right

  • LLM serving systems can sparsify attention with no retraining and no per-request top-k computation, so the savings become an elementwise filter that maps directly onto tiled GPU and distributed kernels.
  • Because the thresholds are stored as tiny per-head tables, sparsity becomes a model parameter that can be shipped alongside the weights and applied immediately at deployment.
  • On memory-bandwidth-bound decoding, cutting V-row reads by 3-10x gives a direct path to latency and throughput gains on long-context generation, where the KV cache dominates.
  • Multi-k calibration enables dynamic switching between sparsity levels at serving time, letting one deployment trade accuracy for speed per request without reloading the model.
  • Post-softmax thresholding with the two compensations can match or slightly exceed full-attention accuracy, consistent with the idea that pruning low-attention noise can help downstream tasks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper leaves implicit that the stability of thresholds across domains suggests thresholds could be calibrated once at pretraining or fine-tuning time and distributed as part of the model release, so downstream users would not need their own calibration set.
  • We infer that the same quantile-based thresholding trick may transfer to other score matrices with stable row distributions, such as mixture-of-expert router logits, though the paper only tests decoder-only LLaMA-style attention.
  • Because V-mean compensation averages over the discarded elements, its error should shrink as sequence length grows, a testable implication the paper does not isolate.
  • If thresholds are truly model-determined, comparing thresholds across many checkpoints of the same architecture could reveal whether they align with interpretable attention-head roles, which the paper does not address.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes Top-Theta (Top-θ) attention, a training-free method that replaces top-k search in transformer attention with static, per-head, per-row thresholds calibrated offline on a small dataset. The thresholds are used to discard low-valued attention elements before the multiplication with the V matrix, reducing V-row reads and, the authors claim, the number of attention elements used during inference. Two compensation mechanisms are introduced: softmax denominator compensation (SDC), which corrects the softmax denominator when pre-softmax sparsification is used, and V-mean compensation (VMC), which replaces discarded V rows by a scaled mean row. The method is evaluated on LLaMA2/3 models from 7B to 70B on ARC, HellaSwag, HumanEval, and LongBench, with thresholds calibrated on one dataset (ARC-C) and applied to others. The central claim is that static thresholds can be calibrated to keep approximately k elements per attention row, yielding up to 10x fewer attention elements and 3–10x fewer V-rows with less than 1% accuracy degradation.

Significance. If the claims hold after correction, the paper makes a useful contribution to efficient LLM inference. The core idea is simple and tiling-friendly, which is a genuine practical advantage over exact top-k selection. The empirical evaluation is broad, covering multiple model sizes, several benchmarks, and cross-domain calibration, and the authors provide source code. The V-row reduction on generative tasks is directly measured and is the most defensible result. The main limitations are that the 'fewer attention elements' claim conflates computed attention scores with retained V-multiplication elements, the VMC proof rests on an unjustified independence assumption, and the threshold-to-k fidelity is not verified in the long-sequence cross-domain regime that produces the headline V-cache savings.

major comments (3)
  1. [Abstract and §4.1/Fig. 4] The claim of 'up to 10× fewer attention elements during inference' is not supported as a computational claim. Thresholding still requires computing all n entries of the attention row qK^T (Eq. 1); for the pre-softmax variants that use exact SDC (§3.2.1), all n discarded exponents must also be summed to compute E. The x-axis of Fig. 4 counts only the retained elements that enter the multiplication with V, not the number of attention-score computations. Please either restate the efficiency claim as a reduction in V-row reads and V-multiplication MACs, or report the full compute including the attention-score computation and the SDC overhead, and relabel the x-axis accordingly.
  2. [§3.2.2 and Appendix E, Eq. (10)] The formal justification of VMC uses the factorization E[s_i V_ij] = E[s_i] E[V_ij] under the assumption s ⊥⊥ V. Since s is a deterministic function of Q and K and V is a deterministic function of X through V = XW^V, both are functions of the same input X, so the unconditional independence is not justified. The remark that they are conditionally independent given X does not imply the displayed unconditional equality, because the expectation in Eq. (10) is not conditioned on X. Please either replace this with a valid derivation (for example, averaging over the random pruning mask conditional on the input) or present VMC as an empirical heuristic without a formal proof.
  3. [§3.1, §3.2, §4.1/Fig. 5, and Appendix B] The paper's fundamental claim that a static threshold 'keeps approximately k elements per row' is verified only in the calibration domain. Appendix B reports the k~/k ratio only for ARC-C, which is also the calibration set for the Q&A tasks. For the cross-domain LongBench experiments with roughly 15k-token prompts, no k~/k ratios or per-row selected-count distributions are reported, even though §3.2 technical detail (i) states that sequence lengths outside the calibration range use the nearest calibrated threshold and Fig. 3 shows noisy threshold estimates at long sequence lengths. Please measure and report the actual per-row selection counts on HumanEval and LongBench, or weaken the 'approximately k elements per row' claim to what is actually measured: downstream accuracy staying within 1%.
minor comments (5)
  1. [§4.1 and Appendix F] The main figures (Figs. 4 and 5) omit error bars; the standard-deviation versions are relegated to Appendix F. Please add error bars to the main figures or move at least one representative panel with error bars into the main text, since the paper uses averaged metrics over samples, layers, and heads.
  2. [Figures throughout] Several figure labels are garbled or truncated (for example, 'Top- ' instead of 'Top-θ' in Figs. 7 and 20, and partially cut axis labels in Fig. 5). Please regenerate the figures with clean, complete labels.
  3. [§4.1] The calibration protocol says '10% of the training or validation sets (different from the test set)'. Please specify for each dataset which split was used and confirm that the ARC-C calibration set used in Figs. 5 and 11 is disjoint from the ARC-C evaluation set used in Fig. 4.
  4. [§3.2.1] The exp-threshold SDC uses a fixed constant γ = 0.05. Please report the sensitivity of the results to this hyperparameter, or justify the fixed value by a calibration experiment.
  5. [§3.2.2] The VMC running-mean update for generative decoding is mentioned in one sentence. Please specify the exact update rule and state whether the initial computation of μ during prefill adds an extra full pass over V for each layer.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: thresholds are calibrated parameters, and all headline efficiency and accuracy numbers are measured on held-out and cross-domain evaluation data rather than being derived from the calibration itself.

full rationale

The paper's core claim is that a static per-head/per-row threshold, calibrated on a few hundred samples, selects approximately k attention elements and preserves downstream accuracy. The threshold is fitted, but it is a method parameter rather than a prediction target: Algorithm 1 computes a quantile-based threshold, and every headline efficiency number (attention-element fraction in Figure 4, V-row fraction in Figures 5 and 18) is measured during inference on held-out evaluation sets, not inferred from the calibrated k. The fact that the calibration-set selection count is approximately k by construction is not presented as an external prediction; the paper explicitly tests transfer to held-out and cross-domain data. The SDC derivation in Eq. (4) is an exact algebraic identity with three stated approximations for E, and the VMC lemma in Appendix E is proven under explicit independence and uniformity assumptions; neither reduces to the result being claimed. There are no self-citations and no imported uniqueness theorems; related work is properly distinguished. The unmeasured k~-to-k fidelity on long out-of-domain sequences, flagged in Section 3.1 and Appendix B, is a generalization-risk concern about an explicitly stated assumption rather than a circular step, because the reported 3x-10x reductions are measured on the actual test tasks. Section 6's limitations (no wall-clock speedups, LLaMA-only experiments) are honest boundary statements, not hidden circularities. No load-bearing step is equivalent to its own input, so the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the empirical stability of attention distributions and on several approximation assumptions in the compensation proofs. The thresholds themselves are calibrated from data, which is a form of fitting, but the held-out evaluation prevents circularity in the accuracy claims. The free parameters are mostly user choices or small hyperparameters.

free parameters (5)
  • k = 32 to 512 (swept)
    User-defined target number of elements per attention row; the paper sweeps k and reports Pareto curves, so it is not fitted to a single value.
  • α (calibration offset) = 0.1 (Figure 2), default not explicitly stated
    Adjusts the calibrated threshold by α standard deviations; a hyperparameter that trades selection count vs. accuracy.
  • γ (SDC exp-threshold constant) = 0.05
    Used in the approximate softmax denominator compensation; hand-chosen to approximate the sum of discarded exponents.
  • Number of calibration samples = 10% of training/validation sets; studied from 8 to 380
    Affects threshold fidelity; the paper recommends a few hundred but does not fix a single value.
  • First two layers k = 512
    Design choice to keep initial layers denser; shown to improve accuracy.
assumptions (4)
  • domain assumption The distribution of attention values for each (layer, head, row) is stable across inputs, domains, and sequence lengths.
    Stated in Section 3.1 as the underlying assumption; load-bearing for the cross-domain and long-sequence claims.
  • domain assumption s is independent of V (attention vector independent of value matrix columns), and the tail distribution of s is uniform, and the expectation of V_j can be approximated by the mean.
    Assumptions in Appendix E proof of VMC; not empirically validated and likely false due to both depending on the input.
  • domain assumption The argmax of the row is included in the selected set I.
    Used in SDC derivation to avoid correcting the max subtraction; not guaranteed for aggressive sparsification.
  • domain assumption Calibration set quantile is a good estimator of the test-time quantile.
    Needed for the threshold to select ~k elements at inference; standard statistical assumption but not proven.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding." pith.science (2026). https://pith.science/paper/SX65M2EK

@misc{pith2026250208363,
  author       = {Pith},
  title        = {Pith review of: Top-Theta Attention: Sparsifying Transformers by Compensated Thresholding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SX65M2EK}},
  note         = {Machine review of arXiv:2502.08363}
}
abstract

We present Top-Theta (Top-$\theta$) Attention, a training-free method for sparsifying transformer attention during inference. Our key insight is that static, per-head thresholds can be calibrated to retain the desired constant number of significant elements per attention row. This approach enables content-based sparsity without retraining, and it remains robust across data domains. We further introduce compensation techniques to preserve accuracy under aggressive sparsification, establishing attention thresholding as a practical and principled alternative to top-k attention. We provide extensive evaluation on natural language processing tasks, showing that Top-$\theta$ achieves 3-10x reduction in V-cache usage and up to 10x fewer attention elements during inference while degrading no more than 1% in accuracy.

Figures

Figures reproduced from arXiv: 2502.08363 by the authors.

Figure 1
Figure 1. Two variants of Top-j attention for inference at generative decoding. Our proposed Top-j attention involves comparing each attention vector against a calibrated threshold. Attention elements that fall below the threshold are pruned away from subsequent computations, enhancing the efficiency and the focus of the model. Our underlying assumption is that a particular distribution of values characterizes each row of the… view at source ↗
Figure 2
Figure 2. Distribution of the set Θ1000, during calibration of threshold θ10,20,800, α = 0.1 on Hellaswag, LLaMA2-7b. During calibration, a “Top-k at calibration” step (l. 8,15) ensures that subsequent layers’ activations re￾flect the sparsification pattern, improving threshold stability at test time. We note that Algorithm 1 in its presented form is calibrating pre-softmax thresholds, whereas it can be adapted to post-softma… view at source ↗
Figure 3
Figure 3. Threshold values as a function of a se￾quence length LLaMA2-7b, 11th transformer layer, calibration targeted k = 64 [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Prefill-based tasks - Tradeoff between model accuracy (y-axis) and the portion of kept attention elements per attention head (x-axis). All post-softmax Top-k and Top-j employ VMC, and all pre-softmax variants employ both VMC and exact SDC. These compensations achieve l…
Figure 5
Figure 5. Figure 5: Generative Tasks – Tradeoff between model accuracy (y-axis) and the portion of required V -rows per group of heads (x-axis). The Top-j variants employ a threshold calibrated on ARC-C dataset. All post-softmax Top-k and Top-j employ VMC, and all pre-softmax variants emp…
Figure 6
Figure 6. Figure 6: Pre- vs. post-softmax Top-k/θ We evaluate the impact of attention matrix sparsifica￾tion in its two main variants: on matrix A and on the post-softmax matrix (S), where each of these two variants requires individual calibration [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: High-k in first two layers We explored the impact of thresholding different lay￾ers with a different target k. For the LLaMA models, we have observed that targeting a slightly higher k in the first layers is crucial. As a best practice, we found keeping 2 initial layer…
Figure 8
Figure 8. Figure 8: Calibrating per-attention-row thresh￾olds vs a unified threshold for all attention rows In Section 4.3, we have shown that Top-j attention should use individually calibrated thresholds for ev￾ery transformer layer in the model due to inherently different attention elem…
Figure 9
Figure 9. Figure 9: SDC and VMC compensations im￾pact the accuracy positively. We evaluate the proposed numerical compensation methods SDC and VMC, finding that more ex￾plicit SDC variants (exp-threshold, exact) substan￾tially recover degraded accuracy on the challeng￾ing Hellaswag task w…
Figure 10
Figure 10. Figure 10: GQA impact - Number of required V -rows for every generated token, LLaMA-3- 8B-Instruct (layer 2, first GQA group), Human￾eval task #25. In GQA, multiple attention heads share the same V matrix; for LLaMA-3-8B models with a group size of g = 4, this means up to 4k V r…
Figure 11
Figure 11. Figure 11: Distribution shift - Top-j calibrated on different task (labeled with cal.arcc) shows comparable accuracy and attention reduction compared to Top-j calibrated on the same task 11a, and compared to Top-k 11b. 5 Related Work A seminal approach in content-based sparsity …
Figure 12
Figure 12. Figure 12: Calibration set size impact - LLaMA2-7B, calibration and evaluation on ARC-C. Left: the Y axis shows the average ratio ˜k/k (the closer to 1 the better the approximation of topk) and the X-axis (different bars) refers to different calibrations that were performed with…
Figure 13
Figure 13. Figure 13: Threshold values as a function of a sequence length in LLaMA2-7b. Left Column: pre-softmax, Right Column: post-softmax. Scatter plots: final calibrated threshold values. Rows top to bottom: layer 0,1,10,31. Purple columns: number of calibration inputs that contained t…
Figure 14
Figure 14. Figure 14: MergeIntervals(Θr) subroutine is merging the set Θr of interval sequences by averaging effective k of all overlapping intervals across all interval sequences in the set Θr 17 [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: MKC calibration of LLaMA2-7b on Hellaswag dataset. Right: the [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]
Figure 16
Figure 16. Figure 16: Prefill-based tasks - Tradeoff between model accuracy averaged across test samples (y-axis), and the portion of kept attention elements per attention head (x-axis). All post-softmax Top-k and Top-j employ VMC, and all pre-softmax variants employ both VMC and exact SDC…
Figure 17
Figure 17. Figure 17: Generative Tasks - during prefill - Tradeoff between model accuracy averaged across test samples (y-axis), and the portion of required attention elements per head (x-axis). The Top-j variants employ threshold calibrated on ARC-C dataset. All post-softmax Top-k and Top…
Figure 18
Figure 18. Figure 18: Generative Tasks - during generative decoding – Tradeoff between model accuracy averaged across test samples (y-axis) and the portion of required V -rows per group of heads (x-axis). The Top-j variants employ threshold calibrated on ARC-C dataset. All post-softmax Top…
Figure 19
Figure 19. Figure 19: Comparison of Pre- and post-softmax thresholding [PITH_FULL_IMAGE:figures/full_fig_p022_19.png]
Figure 20
Figure 20. Figure 20: The positive impact of keeping first two layers dense (higher k for calibration), compared [PITH_FULL_IMAGE:figures/full_fig_p023_20.png]
Figure 21
Figure 21. Figure 21: Calibrating per-attention-row thresholds vs a unified threshold for all rows (sequence [PITH_FULL_IMAGE:figures/full_fig_p024_21.png]
Figure 22
Figure 22. Figure 22: Attention popularity mask – LLaMA-3-8B (GQA group size= 4), Human-eval task number 25, generative decoding iterations as rows. Left – heat map showing how many heads had the corresponding attention element in their Top-128; on the right – the number of V -rows require…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Power Law Guided Dynamic Sifting for Efficient Attention

    cs.LG 2025-06 conditional novelty 6.0 of 10

    SiftAttention skips top-k sorting in sparse attention by thresholding attention weights with a threshold predicted from a power-law fit of score quantiles over generation steps.

Reference graph

Works this paper leans on

39 extracted references · 17 canonical work pages · cited by 1 Pith paper

  1. [1]

    Attention is all you need

    Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in Neural Information Processing Systems, 2017

    work page 2017

  2. [2]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020

    arXiv 2010

  3. [3]

    On the com- putational complexity of self-attention

    Feyza Duman Keles, Pruthuvi Mahesakya Wijewardena, and Chinmay Hegde. On the com- putational complexity of self-attention. In International Conference on Algorithmic Learning Theory, pages 597–619. PMLR, 2023

    work page 2023

  4. [4]

    Model tells you what to discard: Adaptive KV cache compression for llms, 2024

    Suyu Ge, Yunan Zhang, Liyuan Liu, Minjia Zhang, Jiawei Han, and Jianfeng Gao. Model tells you what to discard: Adaptive KV cache compression for llms, 2024. URL https: //arxiv.org/abs/2310.01801

    arXiv 2024

  5. [5]

    Beyond the limits: A survey of techniques to extend the context length in large language models, 2024

    Xindi Wang, Mahsa Salmani, Parsa Omidi, Xiangyu Ren, Mehdi Rezagholizadeh, and Ar- maghan Eshaghi. Beyond the limits: A survey of techniques to extend the context length in large language models, 2024. URL https://arxiv.org/abs/2402.02244

    arXiv 2024

  6. [6]

    A survey on sparsity exploration in transformer- based accelerators

    Kazi Ahmed Asif Fuad and Lizhong Chen. A survey on sparsity exploration in transformer- based accelerators. Electronics, 12(10):2299, 2023

    work page 2023

  7. [7]

    Memory-efficient transformers via Top-k attention

    Ankit Gupta, Guy Dar, Shaya Goodman, David Ciprut, and Jonathan Berant. Memory-efficient transformers via Top-k attention. CoRR, abs/2106.06899, 2021. URL https://arxiv.org/ abs/2106.06899

    arXiv 2021

  8. [8]

    What language model architecture and pretraining objective work best for zero-shot generalization?, 2022

    Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, and Colin Raffel. What language model architecture and pretraining objective work best for zero-shot generalization?, 2022. URL https://arxiv.org/abs/2204.05832

    arXiv 2022

Show all 39 references
  1. [9]

    Roformer: Enhanced transformer with rotary position embedding

    Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 568:127063, 2024. ISSN 0925-2312. doi: https://doi.org/10.1016/j.neucom.2023.127063. URL https://www. sciencedirect.com/sci...

    2024

  2. [10]

    Analyzing the structure of attention in a transformer language model, 2019

    Jesse Vig and Yonatan Belinkov. Analyzing the structure of attention in a transformer language model, 2019. URL https://arxiv.org/abs/1906.04284

    2019 arXiv

  3. [11]

    Linear log-normal attention with unbiased concentration, 2024

    Yury Nahshan, Joseph Kampeas, and Emir Haleva. Linear log-normal attention with unbiased concentration, 2024. URL https://arxiv.org/abs/2311.13541

    2024 arXiv

  4. [12]

    Keep the cost down: A review on methods to optimize LLM’s KV-cache consumption, 2024

    Luohe Shi, Hongyi Zhang, Yao Yao, Zuchao Li, and Hai Zhao. Keep the cost down: A review on methods to optimize LLM’s KV-cache consumption, 2024. URL https://arxiv.org/ abs/2407.18003

    2024 arXiv

  5. [13]

    GQA: Training generalized multi-query transformer models from multi-head checkpoints

    Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. GQA: Training generalized multi-query transformer models from multi-head checkpoints. arXiv preprint arXiv:2305.13245, 2023

    2023 arXiv

  6. [14]

    Parallel Top-K algorithms on GPU: A comprehensive study and new methods

    Jingrong Zhang, Akira Naruse, Xipeng Li, and Yong Wang. Parallel Top-K algorithms on GPU: A comprehensive study and new methods. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC ’23, New York, NY , USA,

  7. [15]

    Towards efficient generative large language model serving: A survey from algorithms to systems, 2023

    Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Xinhao Cheng, Hongyi Jin, Tianqi Chen, and Zhihao Jia. Towards efficient generative large language model serving: A survey from algorithms to systems, 2023. URL https://arxiv.org/abs/2312.15234

    2023 arXiv

  8. [16]

    FlashAttention: Fast and memory-efficient exact attention with io-awareness

    Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. FlashAttention: Fast and memory-efficient exact attention with io-awareness. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems, volume...

    2022

  9. [17]

    A framework for few-shot language model evaluation, 07 2024

    Leo Gao, Jonathan Tow, Baber Abbasi, Stella Biderman, Sid Black, Anthony DiPofi, Charles Foster, Laurence Golding, Jeffrey Hsu, Alain Le Noac’h, Haonan Li, Kyle McDonell, Niklas Muennighoff, Chris Ociepa, Jason Phang, Laria Reynolds, Hailey Schoelkopf, Aviya Skowron, Lintang S...

    2024

  10. [18]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott G...

    2021 arXiv

  11. [19]

    LongBench: A bilingual, multitask benchmark for long context understanding

    Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. LongBench: A bilingual, multitask benchmark for long context understanding. In Proceedings of the 62nd Annual Meeting...

    2024 doi

  12. [20]

    Llama 2: Open foundation and fine-tuned chat models

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288, 2023

    2023 arXiv

  13. [21]

    The llama 3 herd of models

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. arXiv preprint arXiv:2407.21783, 2024

    2024 arXiv

  14. [22]

    Quest: Query-aware sparsity for efficient long-context LLM inference

    Jiaming Tang, Yilong Zhao, Kan Zhu, Guangxuan Xiao, Baris Kasikci, and Song Han. Quest: Query-aware sparsity for efficient long-context LLM inference. arXiv preprint arXiv:2406.10774, 2024

    2024 arXiv

  15. [23]

    Quantization variation: A new perspective on training transformers with low-bit precision

    Xijie Huang, Zhiqiang Shen, Pingcheng Dong, and Kwang-Ting Cheng. Quantization variation: A new perspective on training transformers with low-bit precision. Transactions on Machine Learning Research, 2024. ISSN 2835-8856. URL https://openreview.net/forum?id= MHfoA0Qf6g

    2024

  16. [24]

    Energon: Toward efficient acceleration of transformers using dynamic sparse attention

    Zhe Zhou, Junlin Liu, Zhenyu Gu, and Guangyu Sun. Energon: Toward efficient acceleration of transformers using dynamic sparse attention. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42(1):136–149, 2022

    2022

  17. [25]

    SpAtten: Efficient sparse attention architecture with cascade token and head pruning

    Hanrui Wang, Zhekai Zhang, and Song Han. SpAtten: Efficient sparse attention architecture with cascade token and head pruning. In 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA), pages 97–110. IEEE, 2021

    2021

  18. [26]

    Oh, Yeonhong Park, Yoonho Song, Jung- Hun Park, Sanghee Lee, Kyoung Park, Jae W

    Tae Jun Ham, Sung Jun Jung, Seonghak Kim, Young H. Oh, Yeonhong Park, Yoonho Song, Jung- Hun Park, Sanghee Lee, Kyoung Park, Jae W. Lee, and Deog-Kyoon Jeong. A3: Accelerating attention mechanisms in neural networks with approximation, 2020. URL https://arxiv. org/abs/2002.10941

    2020 arXiv

  19. [27]

    SparQ attention: Bandwidth-efficient LLM inference

    Luka Ribar, Ivan Chelombiev, Luke Hudlass-Galley, Charlie Blake, Carlo Luschi, and Douglas Orr. SparQ attention: Bandwidth-efficient LLM inference. arXiv preprint arXiv:2312.04985, 2023. 13

    2023 arXiv

  20. [28]

    Swin transformer: Hierarchical vision transformer using shifted windows

    Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012–10022, 2021

    2021

  21. [29]

    Learned token pruning for transformers

    Sehoon Kim, Sheng Shen, David Thorsley, Amir Gholami, Woosuk Kwon, Joseph Hassoun, and Kurt Keutzer. Learned token pruning for transformers. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 784–794, 2022

    2022

  22. [30]

    Sparser is faster and less is more: Efficient sparse attention for long-range transformers, 2024

    Chao Lou, Zixia Jia, Zilong Zheng, and Kewei Tu. Sparser is faster and less is more: Efficient sparse attention for long-range transformers, 2024. URL https://arxiv.org/abs/2406. 16747

    2024

  23. [31]

    From softmax to sparsemax: A sparse model of attention and multi-label classification

    Andre Martins and Ramon Astudillo. From softmax to sparsemax: A sparse model of attention and multi-label classification. In Maria Florina Balcan and Kilian Q. Weinberger, editors, Pro- ceedings of The 33rd International Conference on Machine Learning, volume 48 of Proceedings...

    2016

  24. [32]

    Ben Peters, Vlad Niculae, and André F. T. Martins. Sparse sequence-to-sequence models, 2019. URL https://arxiv.org/abs/1905.05702

    2019 arXiv

  25. [33]

    Sparse attention with linear units, 2021

    Biao Zhang, Ivan Titov, and Rico Sennrich. Sparse attention with linear units, 2021. URL https://arxiv.org/abs/2104.07012

    2021 arXiv

  26. [34]

    Peters, and Arman Cohan

    Iz Beltagy, Matthew E. Peters, and Arman Cohan. Longformer: The long-document transformer. CoRR, abs/2004.05150, 2020. URL https://arxiv.org/abs/2004.05150

    2004 arXiv

  27. [35]

    Multi-k Cumulative

    Gyuwan Kim and Kyunghyun Cho. Length-adaptive transformer: Train once with length drop, use anytime with search, 2021. URL https://arxiv.org/abs/2010.07003. 14 A Impact Statement This paper presents work whose goal is to advance the field of efficient Machine Learning. All pot...

    2021 arXiv

  28. [37]

    They are conditionally independent given the input X from which they were originally computed via V = XW V

    s ⊥ ⊥Vj, that is the attention vector s is statistically independent on the elements in the columns of matrix V . They are conditionally independent given the input X from which they were originally computed via V = XW V

  29. [38]

    The distribution of si, ∀i ∈ ¯I within the long tail of the non-selected indices is close to uniform, and hence we can approximate its expectation by an average

  30. [39]

    F Evaluation statistics In this section, we present again the experimental results from Section 4.1; however, to demonstrate statistical significance, we show the error bars

    The expectation of Vij can be approximated by its average. F Evaluation statistics In this section, we present again the experimental results from Section 4.1; however, to demonstrate statistical significance, we show the error bars. This is important since every data point is...

  31. [2023]

    ISBN 9798400701092

    Association for Computing Machinery. ISBN 9798400701092. doi: 10.1145/3581784. 3607062. URL https://doi.org/10.1145/3581784.3607062

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.