9 papers
AdapShot: Adaptive Many-Shot In-Context Learning with Semantic-Aware KV Cache Reuse
Jie Ou, Jinyu Guo, Shiyao Guo +5
Many-Shot In-Context Learning (ICL) has emerged as a promising paradigm, leveraging extensive examples to unlock the reasoning potential of Large Language Models (LLMs). However, e…
CAP: Controllable Alignment Prompting for Unlearning in LLMs
Zhaokun Wang, Jinyu Guo, Jingwen Pu +7
Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance a…
From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors
Yitian Zhou, Chaoning Zhang, Jiaquan Zhang +6
Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context compression an important compone…
ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs
Xunlei Chen, Jinyu Guo, Yuang Li +5
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment…
FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization
Haiyang Xiao, Weiqing Li, Jinyue Guo +3
Although post-training quantization (PTQ) provides an efficient numerical compression scheme for deploying large language models (LLMs) on resource-constrained devices, the represe…
Noise-Robustness Through Noise: A Framework combining Asymmetric LoRA with Poisoning MoE
Zhaokun Wang, Jinyu Guo, Jingwen Pu +5
Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-han…