3 papers
cs.LG2026
TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunications
Pranshav Gajjar, Vijay K Shah
Large language models (LLMs) are increasingly deployed in the telecommunications domain for critical tasks, relying heavily on Retrieval-Augmented Generation (RAG) to adapt general…
cs.LG2026
LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models
Pranshav Gajjar, Manan Tiwari, Sayanta Seth +1
Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting labeled Radio Frequency (RF)…
cs.LG2026
Enhancing Confidence Estimation in Telco LLMs via Twin-Pass CoT-Ensembling
Anton Saenko, Pranshav Gajjar, Abiodun Ganiyu +1
Large Language Models (LLMs) are increasingly applied to complex telecommunications tasks, including 3GPP specification analysis and O-RAN network troubleshooting. However, a criti…