Investigating Acceleration of LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with ‘LITE’ (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have remarkable performance across a wide variety of tasks, however, their large size makes their inference slow and computationally expensive. |
| Approach: | They propose to perform 'dynamic confidence-based early exiting' at token level from the intermediate layers which improves the computational efficiency of text generation without sacrificing the quality of the generation. |
| Outcome: | The proposed model achieves significant cost and quality improvements while maintaining the quality of the generation. |
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Mostafa Elhoushi, Akshat Shrivastava, Diana Liskovich, Basil Hosmer, Bram Wasti, Liangzhen Lai, Anas Mahmoud, Bilge Acun, Saurabh Agarwal, Ahmed Roman, Ahmed Aly, Beidi Chen, Carole-Jean Wu
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| Challenge: | Existing approaches to generate draft tokens in large language models are expensive and resource-intensive. |
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& Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024.acl-long)
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| Challenge: | Existing methods for accelerating Large Language Models have been criticized for their inference costs and inefficient decoding. |
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| Challenge: | Large Language Models have shown extraordinary success across text generation tasks . however, their potential for simple yet essential text classification remains underexplored . |
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| Challenge: | LLms like LLaMA have shown to be cost-effective for generating better responses . however, the instruction-tuned model has only seen one response per instruction . |
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| Challenge: | despite popularity of large language model quantization, there are significant accuracy-performance trade-offs associated with quantization formats. |
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