Improving Efficiency in Large Language Models via Extendable Block Floating Point Representation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are becoming more and more resource-intensive as their size increases. |
| Approach: | They propose a block floating-point (BFP) arithmetic representation that extends the exponent bit width to capture a wider dynamic range. |
| Outcome: | Extendable Exponent Sharing (EES) outperforms representative baselines in accuracy and computational efficiency. |
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