Saved in:
| Main Authors: | Cui, Wanyun, Wang, Qianle |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.02837 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
by: Cui, Wanyun, et al.
Published: (2023)
by: Cui, Wanyun, et al.
Published: (2023)
Homogeneous Keys, Heterogeneous Values: Exploiting Local KV Cache Asymmetry for Long-Context LLMs
by: Cui, Wanyun, et al.
Published: (2025)
by: Cui, Wanyun, et al.
Published: (2025)
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
by: Cheng, Keyuan, et al.
Published: (2024)
by: Cheng, Keyuan, et al.
Published: (2024)
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
by: Xie, Wanyun, et al.
Published: (2025)
by: Xie, Wanyun, et al.
Published: (2025)
Evaluating Quantized Large Language Models
by: Li, Shiyao, et al.
Published: (2024)
by: Li, Shiyao, et al.
Published: (2024)
QWHA: Quantization-Aware Walsh-Hadamard Adaptation for Parameter-Efficient Fine-Tuning on Large Language Models
by: Jeon, Hyesung, et al.
Published: (2025)
by: Jeon, Hyesung, et al.
Published: (2025)
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
by: Huang, Siming, et al.
Published: (2024)
by: Huang, Siming, et al.
Published: (2024)
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
by: Jeon, Hyesung, et al.
Published: (2024)
by: Jeon, Hyesung, et al.
Published: (2024)
The Mercurial Top-Level Ontology of Large Language Models
by: Köhler, Nele, et al.
Published: (2024)
by: Köhler, Nele, et al.
Published: (2024)
On the Compressibility of Quantized Large Language Models
by: Mao, Yu, et al.
Published: (2024)
by: Mao, Yu, et al.
Published: (2024)
TopK Language Models
by: Takahashi, Ryosuke, et al.
Published: (2025)
by: Takahashi, Ryosuke, et al.
Published: (2025)
OutlierTune: Efficient Channel-Wise Quantization for Large Language Models
by: Wang, Jinguang, et al.
Published: (2024)
by: Wang, Jinguang, et al.
Published: (2024)
MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models
by: Cao, Jie, et al.
Published: (2025)
by: Cao, Jie, et al.
Published: (2025)
Distribution-Aware Companding Quantization of Large Language Models
by: Radhakrishnan, Athul, et al.
Published: (2026)
by: Radhakrishnan, Athul, et al.
Published: (2026)
On Context-aware Detection of Cherry-picking in News Reporting
by: Jaradat, Israa, et al.
Published: (2024)
by: Jaradat, Israa, et al.
Published: (2024)
Heterogeneous Value Alignment Evaluation for Large Language Models
by: Zhang, Zhaowei, et al.
Published: (2023)
by: Zhang, Zhaowei, et al.
Published: (2023)
CBQ: Cross-Block Quantization for Large Language Models
by: Ding, Xin, et al.
Published: (2023)
by: Ding, Xin, et al.
Published: (2023)
Quantization of Large Language Models with an Overdetermined Basis
by: Merkulov, Daniil, et al.
Published: (2024)
by: Merkulov, Daniil, et al.
Published: (2024)
BAQ: Efficient Bit Allocation Quantization for Large Language Models
by: Zhang, Chao, et al.
Published: (2025)
by: Zhang, Chao, et al.
Published: (2025)
LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment
by: Zeng, Binrui, et al.
Published: (2024)
by: Zeng, Binrui, et al.
Published: (2024)
Scaling Laws for Post Training Quantized Large Language Models
by: Xu, Zifei, et al.
Published: (2024)
by: Xu, Zifei, et al.
Published: (2024)
Fair-GPTQ: Bias-Aware Quantization for Large Language Models
by: Proskurina, Irina, et al.
Published: (2025)
by: Proskurina, Irina, et al.
Published: (2025)
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
by: Jin, Renren, et al.
Published: (2024)
by: Jin, Renren, et al.
Published: (2024)
Self-Updatable Large Language Models by Integrating Context into Model Parameters
by: Wang, Yu, et al.
Published: (2024)
by: Wang, Yu, et al.
Published: (2024)
Advantageous Parameter Expansion Training Makes Better Large Language Models
by: Gu, Naibin, et al.
Published: (2025)
by: Gu, Naibin, et al.
Published: (2025)
Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation
by: Zhan, Zaifu, et al.
Published: (2025)
by: Zhan, Zaifu, et al.
Published: (2025)
When Quantization Affects Confidence of Large Language Models?
by: Proskurina, Irina, et al.
Published: (2024)
by: Proskurina, Irina, et al.
Published: (2024)
FBQuant: FeedBack Quantization for Large Language Models
by: Liu, Yijiang, et al.
Published: (2025)
by: Liu, Yijiang, et al.
Published: (2025)
DLLMQuant: Quantizing Diffusion-based Large Language Models
by: Xu, Chen, et al.
Published: (2025)
by: Xu, Chen, et al.
Published: (2025)
How Quantization Shapes Bias in Large Language Models
by: Marcuzzi, Federico, et al.
Published: (2025)
by: Marcuzzi, Federico, et al.
Published: (2025)
RUQuant: Towards Refining Uniform Quantization for Large Language Models
by: Liu, Han, et al.
Published: (2026)
by: Liu, Han, et al.
Published: (2026)
Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling
by: Yao, Yuxuan, et al.
Published: (2024)
by: Yao, Yuxuan, et al.
Published: (2024)
Uncertainty Drives Social Bias Changes in Quantized Large Language Models
by: Hua, Stanley Z., et al.
Published: (2026)
by: Hua, Stanley Z., et al.
Published: (2026)
1bit-Merging: Dynamic Quantized Merging for Large Language Models
by: Liu, Shuqi, et al.
Published: (2025)
by: Liu, Shuqi, et al.
Published: (2025)
Using Large Language Models to Construct Virtual Top Managers: A Method for Organizational Research
by: Garzon-Vico, Antonio, et al.
Published: (2026)
by: Garzon-Vico, Antonio, et al.
Published: (2026)
Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
by: Huang, Yichong, et al.
Published: (2024)
by: Huang, Yichong, et al.
Published: (2024)
Precise In-Parameter Concept Erasure in Large Language Models
by: Gur-Arieh, Yoav, et al.
Published: (2025)
by: Gur-Arieh, Yoav, et al.
Published: (2025)
Optimizing Large Language Model Training Using FP4 Quantization
by: Wang, Ruizhe, et al.
Published: (2025)
by: Wang, Ruizhe, et al.
Published: (2025)
Channel-Wise Mixed-Precision Quantization for Large Language Models
by: Chen, Zihan, et al.
Published: (2024)
by: Chen, Zihan, et al.
Published: (2024)
Extreme Compression of Large Language Models via Additive Quantization
by: Egiazarian, Vage, et al.
Published: (2024)
by: Egiazarian, Vage, et al.
Published: (2024)
Similar Items
-
Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
by: Cui, Wanyun, et al.
Published: (2023) -
Homogeneous Keys, Heterogeneous Values: Exploiting Local KV Cache Asymmetry for Long-Context LLMs
by: Cui, Wanyun, et al.
Published: (2025) -
Leveraging Logical Rules in Knowledge Editing: A Cherry on the Top
by: Cheng, Keyuan, et al.
Published: (2024) -
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning
by: Xie, Wanyun, et al.
Published: (2025) -
Evaluating Quantized Large Language Models
by: Li, Shiyao, et al.
Published: (2024)