Papers by Devesh Tiwari
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models (2022.findings-naacl)
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| Challenge: | Recent studies show that the energy requirements of current NLP models are growing at a rapid, unsustainable pace. |
| Approach: | They investigate ways to measure energy usage and different hardware settings that can be tuned to reduce energy consumption for training and inference for language models. |
| Outcome: | The proposed techniques can reduce energy consumption for training and inference for language models. |
Sprout: Green Generative AI with Carbon-Efficient LLM Inference (2024.emnlp-main)
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| Challenge: | Sprout reduces the carbon footprint of inference in large language models by over 40% in real-world evaluations. |
| Approach: | Sprout introduces "generation directives" to guide autoregressive generation process . et al. cites Llama model and global electricity grid data as examples . |
| Outcome: | Sprout reduces the carbon footprint of generative AI models by over 40% in real-world evaluations using the Llama model and global electricity grid data. |