VocabTailor introduces a decoupled dynamic vocabulary selection framework that reduces vocabulary-related memory in SLMs by up to 99% with minimal task performance loss.
A survey of small language models
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FedDetox uses on-device knowledge-distilled classifiers to sanitize toxic data in federated SLM training, preserving safety alignment comparable to centralized baselines.
Qwen-2.5-3B achieves 0.793 accuracy and 988 ms median latency on six-class task routing but misses the pre-registered viability bar of 0.85 accuracy and 2000 ms P95 latency.
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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VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models
VocabTailor introduces a decoupled dynamic vocabulary selection framework that reduces vocabulary-related memory in SLMs by up to 99% with minimal task performance loss.
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FedDetox: Robust Federated SLM Alignment via On-Device Data Sanitization
FedDetox uses on-device knowledge-distilled classifiers to sanitize toxic data in federated SLM training, preserving safety alignment comparable to centralized baselines.
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Evaluating Small Language Models for Front-Door Routing: A Harmonized Benchmark and Synthetic-Traffic Experiment
Qwen-2.5-3B achieves 0.793 accuracy and 988 ms median latency on six-class task routing but misses the pre-registered viability bar of 0.85 accuracy and 2000 ms P95 latency.
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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.