Papers by Ivan Ilin
HIGGS: Pushing the Limits of Large Language Model Quantization via the Linearity Theorem (2025.naacl-long)
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| Challenge: | Existing methods for quantizing large language models focus on breaking down the problem into layer-wise sub-problems and minimizing per-layer error, but this approach lacks theoretical justification and the metrics employed may be sub-optimal. |
| Approach: | They propose a "linearity theorem" establishing a direct relationship between the layer-wise reconstruction error and the model perplexity increase due to quantization. |
| Outcome: | The proposed method outperforms previous data-free methods and improves accuracy-compression trade-offs on Llama-family models. |