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cs.CL2026
Fix the Structural Bottleneck: Context Compression via Explicit Information Transmission
Jiangnan Ye, Hanqi Yan, Zhenyi Shen +3
Long-context LLM agents often struggle with growing token, memory, and latency costs, making efficient context compression essential for practical deployment. Existing LLM-as-a-com…
cs.CL2024
ProSwitch: Knowledge-Guided Instruction Tuning to Switch Between Professional and Non-Professional Responses
Chang Zong, Yuyan Chen, Weiming Lu +4
Large Language Models (LLMs) have demonstrated efficacy in various linguistic applications, including question answering and controlled text generation. However, studies into their…