3 papers
cs.IR2026
Closing the Calibration Gap in Semantic Caching
Aditeya Baral, Radoslav Ralev, Iliya Sotirov Zhechev +2
Semantic caching cuts LLM inference costs by serving a cached response to semantically similar queries. Standard practice evaluates these systems using PR-AUC, a metric that only m…
cs.IR2026
Training for Compositional Sensitivity Reduces Dense Retrieval Generalization
Radoslav Ralev, Aditeya Baral, Iliya Zhechev +2
Dense retrieval compresses texts into single embeddings ranked by cosine similarity. While efficient for recall, this interface is brittle for identity-level matching: minimal comp…
cs.LG2025
Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data
Waris Gill, Justin Cechmanek, Tyler Hutcherson +5
This report investigates enhancing semantic caching effectiveness by employing specialized, fine-tuned embedding models. Semantic caching relies on embedding similarity rather than…