3 papers
cs.CL2026
Relevant but Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard Prompt Compression
Zhengpei Hu, Kai Li, Dapeng Fu +5
Hard prompt compression reduces long-context inference cost by independently scoring tokens, sentences, or chunks and retaining the highest-scoring units under a budget. We identif…
cs.CL2026
BEAVER: A Training-Free Hierarchical Prompt Compression Method via Structure-Aware Page Selection
Zhengpei Hu, Kai Li, Dapeng Fu +4
The exponential expansion of context windows in LLMs has unlocked capabilities for long-document understanding but introduced severe bottlenecks in inference latency and informatio…
cs.CL2024
PLPP: Prompt Learning with Perplexity Is Self-Distillation for Vision-Language Models
Biao Liu, Wenyi Fang, Xiaoyu Wu +3
Pre-trained Vision-Language (VL) models such as CLIP have demonstrated their excellent performance across numerous downstream tasks. A recent method, Context Optimization (CoOp), f…