Papers by Xiliang Zhu
Can Post-Training Quantization Benefit from an Additional QLoRA Integration? (2025.naacl-industry)
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| Challenge: | Large language models require considerable computing resources, which can be costly and often unavailable. |
| Approach: | They propose to integrate 4-bit Post-training Quantization with QLoRA to address these issues . they demonstrate that the integration outperforms standard quantization and fine-tuning . |
| Outcome: | The proposed integration outperforms standard PTQ and 16-bit full-parameter fine-tuning on LLMs. |
How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts? (2025.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) are used for question answering over long contexts . high computational costs and latency hinder the process . |
| Approach: | They explore the capabilities of Large Language Models to answer multiple questions based on the same conversational context. |
| Outcome: | The proposed models outperform proprietary and public models in question answering . their results show that they can be cost-effective and transparent . |