FlipGuard perturbs LLM weights prior to quantization to neutralize quantization-conditioned backdoor attacks, evaluated via the Defense Effectiveness Ratio on multiple models and quantization schemes.
Llm-fp4: 4-bit floating-point quantized transformers
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
Four Over Six adaptively scales blocks in NVFP4 quantization to smaller FP4 values, making representable value distributions more uniform and reducing quantization error especially for near-maximal values.
QuantGuard uses differentiable rounding control to break quantization-boundary backdoors in LLMs, reducing post-quantization attack success to clean-model levels across six models and INT8/FP4/NF4.
LiftQuant enables continuous bit-width LLM quantization via dimensional lifting and projection from a 1-bit lattice, allowing 2.4-bit compression of 70B models that outperforms fixed 2-bit baselines on identical hardware.
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
A 28nm digital CIM accelerator for FP8 uses on-the-fly shift-aware bitwidth prediction, FIFO alignment, and scalable MACs to reach 20.4 TFLOPS/W and 2.8x better efficiency than prior work while supporting variable mantissa widths.
A statistical error analysis framework yields iterative and analytic quantizers that improve accuracy and stability when incorporated into quantization-aware training for integer and floating-point formats.
citing papers explorer
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FlipGuard: Defending Large Language Models Against Quantization-Conditioned Backdoor Attacks
FlipGuard perturbs LLM weights prior to quantization to neutralize quantization-conditioned backdoor attacks, evaluated via the Defense Effectiveness Ratio on multiple models and quantization schemes.
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Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling
Four Over Six adaptively scales blocks in NVFP4 quantization to smaller FP4 values, making representable value distributions more uniform and reducing quantization error especially for near-maximal values.
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Breaking the Rounding Trap: Securing LLMs against Quantization-Conditioned Backdoors
QuantGuard uses differentiable rounding control to break quantization-boundary backdoors in LLMs, reducing post-quantization attack success to clean-model levels across six models and INT8/FP4/NF4.
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LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
LiftQuant enables continuous bit-width LLM quantization via dimensional lifting and projection from a 1-bit lattice, allowing 2.4-bit compression of 70B models that outperforms fixed 2-bit baselines on identical hardware.
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LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
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Balancing FP8 Computation Accuracy and Efficiency on Digital CIM via Shift-Aware On-the-fly Aligned-Mantissa Bitwidth Prediction
A 28nm digital CIM accelerator for FP8 uses on-the-fly shift-aware bitwidth prediction, FIFO alignment, and scalable MACs to reach 20.4 TFLOPS/W and 2.8x better efficiency than prior work while supporting variable mantissa widths.
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StatQAT: Statistical Quantizer Optimization for Deep Networks
A statistical error analysis framework yields iterative and analytic quantizers that improve accuracy and stability when incorporated into quantization-aware training for integer and floating-point formats.