TeleEmbedBench is the first multi-corpus benchmark showing LLM-based embedding models significantly outperform traditional sentence-transformers on telecommunications specifications and code for retrieval accuracy and noise robustness.
Open teleco
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cs.LG 2years
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Twin-Pass Chain-of-Thought Ensembling cuts Expected Calibration Error by up to 88% in Gemma-3 models on TeleQnA, ORANBench, and srsRANBench.
citing papers explorer
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TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
TeleEmbedBench is the first multi-corpus benchmark showing LLM-based embedding models significantly outperform traditional sentence-transformers on telecommunications specifications and code for retrieval accuracy and noise robustness.
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Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
Twin-Pass Chain-of-Thought Ensembling cuts Expected Calibration Error by up to 88% in Gemma-3 models on TeleQnA, ORANBench, and srsRANBench.