Papers by Yuzhuang Xu
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query (2025.emnlp-main)
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| Challenge: | Existing KV cache eviction methods prune tokens using prefilling-stage attention scores, causing inconsistency with actual inference queries. |
| Approach: | They propose a lookahead q-cache framework that generates low-cost pseudo lookaheaded queries to better approximate the true decoding-stage queries. |
| Outcome: | The proposed framework outperforms existing methods on LongBench and Needle-in-a-Haystack benchmarks and can be flexibly combined to yield further improvements. |
ActiView: Evaluating Active Perception Ability for Multimodal Large Language Models (2025.acl-long)
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Ziyue Wang, Chi Chen, Fuwen Luo, Yurui Dong, Yuanchi Zhang, Yuzhuang Xu, Xiaolong Wang, Peng Li, Yang Liu
| Challenge: | Existing benchmarks for evaluating MLLMs have not addressed active perception . a novel benchmark is proposed to evaluate active perception in ML models . |
| Approach: | They propose a benchmark to evaluate active perception in Multimodal Large Language Models . they restrict the perceptual field of a model and require it to actively zoom or shift it . |
| Outcome: | The proposed benchmark focuses on a specialized form of Visual Question Answering (VQA) that eases and quantifies the evaluation yet challenging for existing MLLMs. |
HuoziIME: An On-Device LLM-Enhanced Input Method for Deep Personalization (2026.acl-demo)
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| Challenge: | Mobile input method editors (IMEs) are the primary interface for text input, yet they are constrained to manual typing and struggle to produce personalized text. |
| Approach: | They propose a personalized on-device IME powered by large language models . they endow HUOZIIME with initial human-like prediction ability . |
| Outcome: | The proposed IME has initial human-like prediction ability and is optimized for on-device deployment. |
Perspective Transition of Large Language Models for Solving Subjective Tasks (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized the field of natural language processing . performance of LLMs on subjective tasks is limited, authors say . |
| Approach: | They propose a method that allows LLMs to select between direct, role, and third-person perspectives for best way to solve corresponding subjective problem. |
| Outcome: | The proposed method outperforms widely used single fixed perspective based methods on 12 subjective tasks. |
Pluggable Neural Machine Translation Models via Memory-augmented Adapters (2024.lrec-main)
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| Challenge: | Recent years, neural machine translation systems are often developed with large-scale parallel data extracted from the Web. |
| Approach: | They propose a memory-augmented adapter to steer pretrained neural machine translation models in a pluggable manner by combining model representations and retrieved results. |
| Outcome: | The proposed method outperforms several representative pluggable baselines on style- and domain-specific experiments. |
ArcLight: A Lightweight LLM Inference Architecture for Many-Core CPUs (2026.acl-demo)
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| Challenge: | Existing frameworks for large language model (LLM) inference on CPUs overlook overhead of cross-NUMA memory access. |
| Approach: | They propose a lightweight LLM inference architecture designed from the ground up for many-core CPUs. |
| Outcome: | Experimental results show that ArcLight surpasses the performance ceiling of mainstream frameworks, achieving up to 46% higher inference throughput. |
UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)
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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English. |
| Approach: | They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries. |
| Outcome: | The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation. |