Contextual embedding beyond the gold passage

Perplexity introduces pplx-embed-v2-context-9b-preview, a 9-billion-parameter contextual embedding model designed to retrieve not only answer-bearing text chunks but also the supporting context needed to interpret and verify them within long documents. Rather than relying on conventional single “gold chunk” labels, the model is trained through distillation from a query-aware context compression teacher that assigns continuous relevance scores to document tokens, enabling flexible chunk boundaries and recognition of supporting evidence. It processes documents in a single pass to produce contextual chunk embeddings without added inference-stage reranking or compression costs, and supports 1024- or 2048-dimensional embeddings as well as int8 quantization. The accompanying privately held context-bench benchmark, developed by turbopuffer, evaluates document disambiguation, answer retrieval, and evidence recovery across 2,099 queries and nearly 39,000 long documents from 21 domains. Perplexity reports that its preview model leads evaluated contextual models on context-bench and achieves the highest average performance among shown models on the public ConTEB benchmark, while retaining competitive general retrieval performance and showing limited sensitivity to chunk size. A preview is available through Hugging Face, while context-bench evaluations can be requested from turbopuffer to reduce benchmark contamination.

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