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- Search for Efficient Large Language Model - proceedings. neurips. cc
Abstract realms of artificial intelligence research Numerous efficient techniques, including weight pruning, quantization, and distillation, have been embraced to compress LLMs, targeting memory reduction and inference accelerati
- [2405. 11704] Efficiency optimization of large-scale language models . . .
In terms of model deployment and inference optimization, this paper systematically reviews the latest advances in model compression techniques, focusing on strategies such as quantification, pruning, and knowledge distillation
- ICML 2025 Papers
ELMO : Efficiency via Low-precision and Peak Memory Optimization in Large Output Spaces Online Learning in Risk Sensitive constrained MDP AdaWorld: Learning Adaptable World Models with Latent Actions Unifying Knowledge from Diverse Datasets to Enhance Spatial-Temporal Modeling: A Granularity-Adaptive Geographical Embedding Approach
- Optimizing large language models: Techniques for efficiency and . . .
This section presents the overview results of our research on optimizing large language models (LLMs) focusing on training time, performance metrics, memory usage, inference time, and scalability
- Efficiency optimization of large-scale language models based on deep . . .
Through this research, it is expected to stimulate more innovative ideas and solutions for efficiency optimization of large-scale language models, and jointly promote the green transformation of
- Optimizing Large Language Models: A Deep Dive into Effective Prompt . . .
This paper analyzes various Prompt Engineering techniques for large-scale language models and identifies methods that can optimize response performance across different datasets without the need for extensive retraining or fine-tuning
- [2309. 03409] Large Language Models as Optimizers - arXiv. org
In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language
- An empirical analysis of compute-optimal large language model . . . - NeurIPS
We find that current large language models are significantly undertrained, a consequence of the recent focus on scaling language models whilst keeping the amount of training data constant
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