library_name: transformers
tags: []
Model Description
Comp4Cls is a retrieval-augmented classification framework that uses entity-centric semantic compression to turn long scientific/technical documents into short, task-focused representations for both retrieval and labeling. Documents (papers, patents, and R&D reports) are first compressed into structured summaries that preserve discriminative signals (e.g., core concepts, methods, problems, findings), embedded, and stored in a vector DB. At inference, a query is compressed the same way, nearest neighbors are retrieved, and a small LLM assigns the final class label using the compressed evidence.
The end-to-end workflow—Phase 1: compression + indexing, Phase 2: retrieval + classification—is illustrated in the framework diagram on page 2. Experiments on a large bilingual corpus with hierarchical, multi-label taxonomies show that a 4B-scale Comp4Cls matches or outperforms 8B–14B models, especially in fine-grained categories, while cutting token usage and compute. Moderate compression (often ~20% of entities) preserves retrieval fidelity and boosts downstream F1, enabling lightweight, low-latency deployment in production pipelines. See Table II on page 8 (compression vs. length), Figure 6 on page 9 (retrieval quality under compression), and Figure 7 on page 10 (accuracy vs. larger LLMs).
Framework Diagram
Framework Diagram
Figure 1. Two-phase pipeline: compression/indexing then retrieval/classification.
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