PCB-QA is the first QA benchmark for LLMs on printed circuit board designs, with Gemini 3 Flash Preview reaching 93% accuracy on a JSON textual representation.
arXiv preprint arXiv:2404.09135 , year=
4 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Pith papers citing it
19
external citations · external index
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Mainstream UQ for LLMs reduces to unsupervised clustering of internal generation consistency and therefore cannot detect confident hallucinations or provide reliable safety signals.
Federated QLoRA fine-tuning on distributed PA manuals from SIGESON and SIDFORS yields ROUGE-1/2/L of 61.10/55.77/59.44 and BLEU-4 of 45.02, close to centralized training.
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.
citing papers explorer
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PCB-QA: Evaluating LLMs over the First Printed Circuit Board Design Question-Answer Dataset
PCB-QA is the first QA benchmark for LLMs on printed circuit board designs, with Gemini 3 Flash Preview reaching 93% accuracy on a JSON textual representation.
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Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
Mainstream UQ for LLMs reduces to unsupervised clustering of internal generation consistency and therefore cannot detect confident hallucinations or provide reliable safety signals.
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GuidaPA: Privacy-Preserving Chatbot for Public Administration via Federated Learning
Federated QLoRA fine-tuning on distributed PA manuals from SIGESON and SIDFORS yields ROUGE-1/2/L of 61.10/55.77/59.44 and BLEU-4 of 45.02, close to centralized training.
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DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs
DP-FLogTinyLLM combines federated learning, differential privacy, and LoRA-tuned tiny LLMs to match centralized log anomaly detection performance on Thunderbird and BGL datasets while preserving privacy.