In-context Autoencoder for Context Compression in a Large Language Model
Fuente:
arXiv
Saved in:
| Main Authors: | Ge, Tao, Hu, Jing, Wang, Lei, Wang, Xun, Chen, Si-Qing, Wei, Furu |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Textual Aesthetics in Large Language Models
by: Jiang, Lingjie, et al.
Published: (2024)
by: Jiang, Lingjie, et al.
Published: (2024)
An Evaluation on Large Language Model Outputs: Discourse and Memorization
by: de Wynter, Adrian, et al.
Published: (2023)
by: de Wynter, Adrian, et al.
Published: (2023)
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
by: Cheng, Xin, et al.
Published: (2024)
by: Cheng, Xin, et al.
Published: (2024)
Multi-Head Mixture-of-Experts
by: Wu, Xun, et al.
Published: (2024)
by: Wu, Xun, et al.
Published: (2024)
Dynamic Universal Approximation Theory: The Basic Theory for Transformer-based Large Language Models
by: Wang, Wei, et al.
Published: (2024)
by: Wang, Wei, et al.
Published: (2024)
Auto-ICL: In-Context Learning without Human Supervision
by: Yang, Jinghan, et al.
Published: (2023)
by: Yang, Jinghan, et al.
Published: (2023)
On the Compressibility of Quantized Large Language Models
by: Mao, Yu, et al.
Published: (2024)
by: Mao, Yu, et al.
Published: (2024)
Guiding LLM Post-training Data Engineering with Model Internals from Sparse Autoencoders
by: Jing, Yi, et al.
Published: (2026)
by: Jing, Yi, et al.
Published: (2026)
Learning to Compress Prompt in Natural Language Formats
by: Chuang, Yu-Neng, et al.
Published: (2024)
by: Chuang, Yu-Neng, et al.
Published: (2024)
GUNDAM: Aligning Large Language Models with Graph Understanding
by: Ouyang, Sheng, et al.
Published: (2024)
by: Ouyang, Sheng, et al.
Published: (2024)
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression
by: Huang, Wei, et al.
Published: (2025)
by: Huang, Wei, et al.
Published: (2025)
Detecting Data Contamination from Reinforcement Learning Post-training for Large Language Models
by: Tao, Yongding, et al.
Published: (2025)
by: Tao, Yongding, et al.
Published: (2025)
Model Compression and Efficient Inference for Large Language Models: A Survey
by: Wang, Wenxiao, et al.
Published: (2024)
by: Wang, Wenxiao, et al.
Published: (2024)
Binary Autoencoder for Mechanistic Interpretability of Large Language Models
by: Cho, Hakaze, et al.
Published: (2025)
by: Cho, Hakaze, et al.
Published: (2025)
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework
by: Weng, Jiaqi, et al.
Published: (2025)
by: Weng, Jiaqi, et al.
Published: (2025)
MiniCache: KV Cache Compression in Depth Dimension for Large Language Models
by: Liu, Akide, et al.
Published: (2024)
by: Liu, Akide, et al.
Published: (2024)
Are Large Language Models Table-based Fact-Checkers?
by: Zhang, Hanwen, et al.
Published: (2024)
by: Zhang, Hanwen, et al.
Published: (2024)
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
by: Qiao, Boyu, et al.
Published: (2026)
by: Qiao, Boyu, et al.
Published: (2026)
LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit
by: Gong, Ruihao, et al.
Published: (2024)
by: Gong, Ruihao, et al.
Published: (2024)
Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models
by: Zhuo, Zhijian, et al.
Published: (2024)
by: Zhuo, Zhijian, et al.
Published: (2024)
Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning
by: Wang, Xinyi, et al.
Published: (2023)
by: Wang, Xinyi, et al.
Published: (2023)
SelfCite: Self-Supervised Alignment for Context Attribution in Large Language Models
by: Chuang, Yung-Sung, et al.
Published: (2025)
by: Chuang, Yung-Sung, et al.
Published: (2025)
Probing the Decision Boundaries of In-context Learning in Large Language Models
by: Zhao, Siyan, et al.
Published: (2024)
by: Zhao, Siyan, et al.
Published: (2024)
Revisiting In-context Learning Inference Circuit in Large Language Models
by: Cho, Hakaze, et al.
Published: (2024)
by: Cho, Hakaze, et al.
Published: (2024)
K-Level Reasoning: Establishing Higher Order Beliefs in Large Language Models for Strategic Reasoning
by: Zhang, Yadong, et al.
Published: (2024)
by: Zhang, Yadong, et al.
Published: (2024)
On Meta-Prompting
by: de Wynter, Adrian, et al.
Published: (2023)
by: de Wynter, Adrian, et al.
Published: (2023)
Systematic Outliers in Large Language Models
by: An, Yongqi, et al.
Published: (2025)
by: An, Yongqi, et al.
Published: (2025)
A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models
by: Shu, Dong, et al.
Published: (2025)
by: Shu, Dong, et al.
Published: (2025)
Sparse Shift Autoencoders for Identifying Concepts from Large Language Model Activations
by: Joshi, Shruti, et al.
Published: (2025)
by: Joshi, Shruti, et al.
Published: (2025)
MathScale: Scaling Instruction Tuning for Mathematical Reasoning
by: Tang, Zhengyang, et al.
Published: (2024)
by: Tang, Zhengyang, et al.
Published: (2024)
Large Language Models are Miscalibrated In-Context Learners
by: Li, Chengzu, et al.
Published: (2023)
by: Li, Chengzu, et al.
Published: (2023)
From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models
by: Xu, Chejian, et al.
Published: (2025)
by: Xu, Chejian, et al.
Published: (2025)
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
by: Pang, Jing-Cheng, et al.
Published: (2024)
by: Pang, Jing-Cheng, et al.
Published: (2024)
PPC-GPT: Federated Task-Specific Compression of Large Language Models via Pruning and Chain-of-Thought Distillation
by: Fan, Tao, et al.
Published: (2025)
by: Fan, Tao, et al.
Published: (2025)
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
by: Hua, Wenyue, et al.
Published: (2024)
by: Hua, Wenyue, et al.
Published: (2024)
Quantifying Feature Space Universality Across Large Language Models via Sparse Autoencoders
by: Lan, Michael, et al.
Published: (2024)
by: Lan, Michael, et al.
Published: (2024)
Evaluating the Factuality of Large Language Models using Large-Scale Knowledge Graphs
by: Liu, Xiaoze, et al.
Published: (2024)
by: Liu, Xiaoze, et al.
Published: (2024)
Clustering-driven Memory Compression for On-device Large Language Models
by: Bohdal, Ondrej, et al.
Published: (2026)
by: Bohdal, Ondrej, et al.
Published: (2026)
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
by: Chen, Guoxuan, et al.
Published: (2024)
by: Chen, Guoxuan, et al.
Published: (2024)
Eliciting In-context Retrieval and Reasoning for Long-context Large Language Models
by: Qiu, Yifu, et al.
Published: (2025)
by: Qiu, Yifu, et al.
Published: (2025)
Similar Items
-
Textual Aesthetics in Large Language Models
by: Jiang, Lingjie, et al.
Published: (2024) -
An Evaluation on Large Language Model Outputs: Discourse and Memorization
by: de Wynter, Adrian, et al.
Published: (2023) -
xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token
by: Cheng, Xin, et al.
Published: (2024) -
Multi-Head Mixture-of-Experts
by: Wu, Xun, et al.
Published: (2024) -
Dynamic Universal Approximation Theory: The Basic Theory for Transformer-based Large Language Models
by: Wang, Wei, et al.
Published: (2024)