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.
Telecomgpt: A framework to build telecom-specific large language models
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The paper introduces a multi-agent LLM framework using ReAct-style agents with self-reflection for classifying telecom queries, anonymizing PII via k-anonymity and differential privacy, and translating expert responses for end users.
Domain-adapted LLMs and SLMs do not consistently outperform general models on STRIDE threat classification for 5G, with decoding strategies and model scale affecting validity but gains remaining insufficient for reliable use.
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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Cross-Domain Query Translation for Network Troubleshooting: A Multi-Agent LLM Framework with Privacy Preservation and Self-Reflection
The paper introduces a multi-agent LLM framework using ReAct-style agents with self-reflection for classifying telecom queries, anonymizing PII via k-anonymity and differential privacy, and translating expert responses for end users.
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Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insights
Domain-adapted LLMs and SLMs do not consistently outperform general models on STRIDE threat classification for 5G, with decoding strategies and model scale affecting validity but gains remaining insufficient for reliable use.