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SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models

Ziwei Li, Yuang Ma, Yi Kang · Apr 6, 2026 · Citations: 0

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How to use this paper page

Coverage: Recent

Use this page to decide whether the paper is strong enough to influence an eval design. It summarizes the abstract plus available structured metadata. If the signal is thin, use it as background context and compare it against stronger hub pages before making protocol choices.

Best use

Background context only

Metadata: Recent

Trust level

Provisional

Signals: Recent

What still needs checking

Structured extraction is still processing; current fields are metadata-first.

Signal confidence unavailable

Abstract

The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands. While model compression, such as network pruning, offers potential solutions, most existing methods often fail to maintain good performance at high compression ratios. To address this, we propose SLaB, a novel framework that decomposes each linear layer weight into three complementary components: a sparse matrix, a low-rank matrix, and a binary matrix. SLaB eliminates the need for retraining and leverages activation-aware pruning scores to guide the decomposition process. Experiments on Llama-family models demonstrate that SLaB achieves state-of-the-art performance, reducing perplexity by up to 36% compared to existing methods at 50% compression and improving accuracy by up to 8.98% over the baseline on zero-shot tasks.

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Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

Structured extraction is still processing; current fields are metadata-first.

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HFEPX Relevance Assessment

Signal extraction is still processing. This page currently shows metadata-first guidance until structured protocol fields are ready.

Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

Structured extraction is still processing; current fields are metadata-first.

Trust level

Provisional

Eval-Fit Score

Unavailable

Eval-fit score is unavailable until extraction completes.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Provisional (processing)

Extraction confidence: Provisional

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

What This Page Found In The Paper

Each field below shows whether the signal looked explicit, partial, or missing in the available metadata. Use this to judge what is safe to trust directly and what still needs full-paper validation.

Human Feedback Types

provisional

None explicit

Confidence: Provisional Best-effort inference

No explicit feedback protocol extracted.

Evidence snippet: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.

Evaluation Modes

provisional

Automatic metrics

Confidence: Provisional Best-effort inference

Includes extracted eval setup.

Evidence snippet: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.

Quality Controls

provisional

Not reported

Confidence: Provisional Best-effort inference

No explicit QC controls found.

Evidence snippet: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.

Benchmarks / Datasets

provisional

Not extracted

Confidence: Provisional Best-effort inference

No benchmark anchors detected.

Evidence snippet: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.

Reported Metrics

provisional

Accuracy

Confidence: Provisional Best-effort inference

Useful for evaluation criteria comparison.

Evidence snippet: Experiments on Llama-family models demonstrate that SLaB achieves state-of-the-art performance, reducing perplexity by up to 36% compared to existing methods at 50% compression and improving accuracy by up to 8.98% over the baseline on zero-shot tasks.

Rater Population

provisional

Unknown

Confidence: Provisional Best-effort inference

Rater source not explicitly reported.

Evidence snippet: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.

Human Data Lens

This page is using abstract-level cues only right now. Treat the signals below as provisional.

Potential human-data signal: No explicit human-data keywords detected.
Potential benchmark anchors: No benchmark names detected in abstract.
Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Lens

Evaluation fields are inferred from the abstract only.

Potential evaluation modes: Automatic metrics
Potential metric signals: Accuracy
Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massive computational and memory demands.
While model compression, such as network pruning, offers potential solutions, most existing methods often fail to maintain good performance at high compression ratios.
To address this, we propose SLaB, a novel framework that decomposes each linear layer weight into three complementary components: a sparse matrix, a low-rank matrix, and a binary matrix.

Researcher Actions

Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
Validate inferred eval signals (Automatic metrics) against the full paper.
Use related-paper links to find stronger protocol-specific references.

Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

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