comp4cls
/

comp4cls-4B

@@ -17,162 +17,34 @@ The end-to-end workflow—**Phase 1: compression + indexing, Phase 2: retrieval
   <em>Figure 1. Overview of the **Comp4Cls** framework. The system operates in two phases: (i) documents with predefined class labels are semantically compressed, embedded, and stored in a vector database; (ii) when a new query arrives, it is compressed and used to retrieve the top-$k$ most similar documents from the vector store. The large language model (LLM) then determines the final class label based on the retrieved context. Finally, the compressed query and its assigned label are stored back into the database, enabling downstream services such as document categorization, semantic search, and TL;DR summarization.</em>
 </p>
-## Model Details
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 ## Citation [optional]

   <em>Figure 1. Overview of the **Comp4Cls** framework. The system operates in two phases: (i) documents with predefined class labels are semantically compressed, embedded, and stored in a vector database; (ii) when a new query arrives, it is compressed and used to retrieve the top-$k$ most similar documents from the vector store. The large language model (LLM) then determines the final class label based on the retrieved context. Finally, the compressed query and its assigned label are stored back into the database, enabling downstream services such as document categorization, semantic search, and TL;DR summarization.</em>
 </p>
+# Key Features
+* **Entity-centric Semantic Compression**
+  Two-stage prompting (entity extraction → selective rewriting) produces concise, structured summaries that retain label-relevant semantics while removing redundancy. The compressor exposes an explicit **compression ratio** to match accuracy/latency budgets.
+* **Retrieval-Augmented Classification (RAG) with Short Contexts**
+  Operates on compressed texts for both the query and neighbors, reducing context length and enabling **broader top-k** without “lost-in-the-middle” degradation.
+* **Small-Model, Big-Model Performance**
+  With **~20% compression**, a **4B** backbone achieves or exceeds the accuracy of **8B–14B** models across domains and taxonomy levels.
+* **Provable Efficiency Gains**
+  Compression reduces input tokens by **~50%** on average while maintaining semantic similarity; retrieval accuracy remains near full-text levels.
+* **Scales to Real-World, Heterogeneous Corpora**
+  Trained/evaluated on large bilingual datasets spanning **papers, patents, and R&D reports** with hierarchical, multi-label taxonomies; robust under domain shift and taxonomy changes.
+* **Production-minded Latency/Throughput**
+  Shorter prompts cut classification-stage latency; compression allows higher **top-k (≈20–30)** before context saturation.
+* **Vector DB-Ready Artifacts**
+  Outputs compressed texts + embeddings that plug into standard ANN indices (e.g., HNSW) for high-throughput retrieval in enterprise knowledge systems.
+* **Beyond Classification**
+  The compressed representations support downstream **semantic search**, **TL;DR summaries**, and **knowledge organization** tasks out of the box.
 ## Citation [optional]