Papers by Xuefei Ning
How Quantization Shapes Bias in Large Language Models (2026.eacl-long)
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| Challenge: | a systematic review of quantization's effects on model biases focuses on stereotypes, fairness, toxicity, and sentiment. |
| Approach: | They focus on weight and activation quantization strategies and examine their effects across bias types including stereotypes, fairness, toxicity, and sentiment. |
| Outcome: | The proposed method can reduce stereotypes and unfairness, but it tends to increase stereotypes in generative tasks. |
Efficient Inference for Large Language Models –Algorithm, Model, and System (2025.emnlp-tutorials)
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| Challenge: | Inference of LLMs incurs high computational costs, memory access overhead, and memory usage, leading to inefficiencies in terms of latency, throughput, power consumption, and storage. |
| Approach: | This tutorial introduces the basics of efficient inference for LLMs and explains how to diagnose efficiency bottlenecks for a given workload on specific hardware. |
| Outcome: | The tutorial introduces the basic concepts of modern LLMs, software and hardware. |