MedTPE compresses EHR token sequences by up to 31% via merging common medical token pairs, reducing LLM inference latency 34-63% while maintaining or improving performance on mortality and phenotyping tasks.
Biomedlm: A 2.7 b parameter lan- guage model trained on biomedical text.arXiv preprint arXiv:2403.18421
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
verdicts
UNVERDICTED 3roles
background 2polarities
background 2representative citing papers
Fine-tuned LLaMA 3.1-8B variants for the energy sector outperform the base model on domain QA benchmarks, with LoRA delivering similar gains at lower training cost.
Position paper claiming that distributed training across massive edge devices can overcome data depletion and centralized compute monopolies in LLM scaling.
citing papers explorer
-
From Token to Token Pair: Efficient Prompt Compression for Large Language Models in Clinical Prediction
MedTPE compresses EHR token sequences by up to 31% via merging common medical token pairs, reducing LLM inference latency 34-63% while maintaining or improving performance on mortality and phenotyping tasks.
-
Towards EnergyGPT: A Large Language Model Specialized for the Energy Sector
Fine-tuned LLaMA 3.1-8B variants for the energy sector outperform the base model on domain QA benchmarks, with LoRA delivering similar gains at lower training cost.
-
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.