Papers by Jae-Joon Kim
L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models (2025.acl-long)
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| Challenge: | Quantization-aware PEFT methods have been developed to reduce memory and computational costs associated with large language models. |
| Approach: | They propose a method that integrates Quantization-Aware Training (QAT) with LoRA to reduce memory overhead and improve model accuracy. |
| Outcome: | The proposed method significantly reduces QAT’s memory overhead while preserving the advantage of QAT in producing fully quantized LLMs with high accuracy. |
FastKV: Decoupling of Context Reduction and KV Cache Compression for Prefill-Decoding Acceleration (2026.findings-acl)
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| Challenge: | Large language models (LLMs) excel at handling long-context sequences, but require substantial prefill computation and key-value (KV) cache. |
| Approach: | They propose a KV cache compression framework that decouples prefill computation from decoding KV budget. |
| Outcome: | The proposed framework reduces latency in prefill and decoding by leveraging the stabilization of token importance in later layers. |