AI RESEARCH
No Mean Feat: Simple, Strong Baselines for Context Compression
arXiv CS.AI
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ArXi:2510.20797v2 Announce Type: replace-cross Context compression reduces Transformer inference costs by replacing lengthy inputs with shorter pre-computed representations. It carries significant benefits for retrieval-augmented generation (RAG) and has attracted growing research attention. However, progress remains difficult to measure due to inconsistent evaluations and baselines. We design a standard, easy-to-reproduce evaluation suite for context compression, BenchPress, along with simple, high-performance baselines for English reading comprehension.