Looped language models with latent iterative computation and entropy-regularized depth allocation achieve performance matching up to 12B standard LLMs through superior knowledge manipulation.
Chinese tiny llm: Pretraining a chinese-centric large language model
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EvalMORAAL evaluates moral alignment of 20 LLMs on World Values Survey and PEW data, reporting high overall correlation with human responses but a 0.21 gap between Western and non-Western regions.
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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Scaling Latent Reasoning via Looped Language Models
Looped language models with latent iterative computation and entropy-regularized depth allocation achieve performance matching up to 12B standard LLMs through superior knowledge manipulation.
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EvalMORAAL: Interpretable Chain-of-Thought and LLM-as-Judge Evaluation for Moral Alignment in Large Language Models
EvalMORAAL evaluates moral alignment of 20 LLMs on World Values Survey and PEW data, reporting high overall correlation with human responses but a 0.21 gap between Western and non-Western regions.
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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.