Papers with LLaMA-3.2-1B
Comparing Text Compression Capabilities of Large Language Models with Traditional Compression Algorithms (2026.eacl-srw)
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| Challenge: | Experimental results show that large language models outperform baselines on non-English datasets . traditional methods remained dataset-agnostic, and the results suggest that current methods are impractical for the compression task. |
| Approach: | They evaluate the non-English and unstructured text compression performance of Large Language Models . they compare them with traditional baselines on datasets from eight most widely spoken languages . |
| Outcome: | The evaluated LLM outperformed baselines on non-English datasets . the results show that the current methods are highly impractical for the compression task . |
Are Large Language Models Economically Viable for Industry Deployment? (2026.acl-industry)
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Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora, Rafiq Ali, Ebad Shabbir, Gautam Siddharth Kashyap, Jiechao Gao, Usman Naseem
| Challenge: | Generative AI is increasingly deployed in healthcare, financial analytics, and conversational automation. |
| Approach: | They propose a framework that evaluates large language models across their full lifecycle on legacy GPUs. |
| Outcome: | The proposed framework evaluates LLMs across their full lifecycle on legacy GPUs. |
CARVQ: Corrective Adaptor with Group Residual Vector Quantization for LLM Embedding Compression (2025.findings-emnlp)
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Dayin Gou, Sanghyun Byun, Nilesh Malpeddi, Gabrielle De Micheli, Prathamesh Vaste, Jacob Song, Woo Seong Chung
| Challenge: | Large Language Models typically rely on a large number of parameters for token embedding, leading to substantial storage requirements and memory footprints. |
| Approach: | They propose a corrective Adaptor with group Residual Vector Quantization that can be used to compress the embedding layer without requiring specialized hardware. |
| Outcome: | The proposed corrective adaptor can achieve lower average bitwidth-per-parameter while maintaining reasonable perplexity and accuracy compared to scalar quantization. |