2 papers
cs.CL2026
Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs
Shenglai Zeng, Tianqi Zheng, Chuan Tian +10
Personalizing large language models (LLMs) to individual users requires incorporating extensive interaction histories and profiles, but input token constraints make this impractica…
cs.CL2024
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM
Ruohong Zhang, Yau-Shian Wang, Yiming Yang
The remarkable performance of large language models (LLMs) in zero-shot language understanding has garnered significant attention. However, employing LLMs for large-scale inference…