TeleCom-Bench reveals LLMs reach 90% on telecom intent and entity tasks but drop to 30% on solution generation and root cause analysis in live network scenarios.
Injecting domain-specific knowledge into large language models: a comprehensive survey
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G-reasoner uses QuadGraph abstraction and a 34M-parameter graph foundation model integrated with LLMs to enable scalable reasoning over diverse graph-structured knowledge, outperforming baselines on six benchmarks.
CORE is a lightweight two-stage prompt compression method for edge-device RAG QA that builds answer and clue sets via NER and semantic matching then refines them to deliver higher accuracy and lower resource costs than baselines.
KnowPilot integrates knowledge retrieval and memory systems into generative agents to achieve better results on domain-specific tasks such as text generation.
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.
citing papers explorer
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TeleCom-Bench: How Far Are Large Language Models from Industrial Telecommunication Applications?
TeleCom-Bench reveals LLMs reach 90% on telecom intent and entity tasks but drop to 30% on solution generation and root cause analysis in live network scenarios.
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G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
G-reasoner uses QuadGraph abstraction and a 34M-parameter graph foundation model integrated with LLMs to enable scalable reasoning over diverse graph-structured knowledge, outperforming baselines on six benchmarks.
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Less is More: Lightweight Prompt Compression for Question Answering Applications on Edge Devices
CORE is a lightweight two-stage prompt compression method for edge-device RAG QA that builds answer and clue sets via NER and semantic matching then refines them to deliver higher accuracy and lower resource costs than baselines.
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KnowPilot: Your Knowledge-Driven Copilot for Domain Tasks
KnowPilot integrates knowledge retrieval and memory systems into generative agents to achieve better results on domain-specific tasks such as text generation.
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Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.