Papers by Xuefei Wang
Resonating with RoPE: Spectral Quantization for High-Fidelity Key Cache Compression (2026.acl-long)
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| Challenge: | Existing approaches to reduce memory footprint of long-context LLMs rely on RoPE-induced oscillations. |
| Approach: | They propose a frequency-domain framework that converts RoPE-induced oscillations into sparse spectral representations. |
| Outcome: | The proposed framework achieves efficient compression with performance comparable to FP16 benchmarks. |
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention (C18-1)
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| Challenge: | Existing topic models ignore that one discusses diverse topics when dynamically interacting with different people. |
| Approach: | They propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. |
| Outcome: | The proposed model is based on three real-world microblog datasets. |
We Need to Talk About Reproducibility in NLP Model Comparison (2023.emnlp-main)
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| Challenge: | Existing studies show that standard splits produce low reproducible and unreliable conclusions . reproducibility of empirical experimental conclusions is a problem in NLP domain . |
| Approach: | They propose to transform the reproducibility of a model comparison into a probabilistic function . they propose to use a regularized corpus splitting strategy to estimate the model's performance . |
| Outcome: | The proposed estimator achieves a high SNR and significantly increases reproducibility. |
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. |