Papers by Thanh-Toan Do
MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora (2025.emnlp-main)
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| Challenge: | Existing approaches to update model-based indexes with new documents are expensive and require expensive retraining. |
| Approach: | They propose a framework that combines an expandable mixture of Low-Rank Adaptation experts with a layer-wise out-of-distribution-driven expansion strategy. |
| Outcome: | Experiments on NQ320k and MS MARCO Passage show that the proposed framework outperforms full-model update baselines with minimal parameter overhead and substantially lower training costs. |
Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models (2026.acl-long)
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| Challenge: | Existing methods to quantize large language models suffer from significant accuracy loss at low bit-widths due to high-impact parameters. |
| Approach: | They propose a quadratic optimization framework that quantizes high-impact parameters to moderate bit-widths while quantizing low bit-wideths. |
| Outcome: | The proposed framework preserves high-impact parameters while preserving memory usage. |