Papers by Xiaowen Liu
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
Copied to clipboard
Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation (2025.findings-acl)
Copied to clipboard
SongTang SongTang, Kaiyong Zhao, Lei Wang, Yuliang Li, Xuebo Liu, Junyi Zou, Qiang Wang, Xiaowen Chu
| Challenge: | UnrealLLM is a novel framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. |
| Approach: | They propose a novel multi-agent framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. |
| Outcome: | The proposed framework achieves competitive performance in technical metrics and aesthetic quality, offering unique advantages in generation scale and interactivity. |
Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not Stable (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have similar value rankings but little is known about how susceptible they are to external influence and how different values are correlated with each other. |
| Approach: | They propose to use 6 different value transformation prompting methods to examine the plasticity of LLM value systems by comparing them with 8 LLMs. |
| Outcome: | The proposed methods are effective on 8 LLMs and 3 families. |
LPZero: Language Model Zero-cost Proxy Search from Zero (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing zero-cost (ZC) proxies rely on expert knowledge and incur significant trial-and-error costs. |
| Approach: | They propose a framework that automatically designs zero-cost (ZC) proxies for various tasks and incorporates genetic programming to find the optimal symbolic composition. |
| Outcome: | The proposed framework achieves higher ranking consistency than human-designed proxies on NLP tasks. |
Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research (2025.findings-emnlp)
Copied to clipboard
Xiang Liu, Penglei Sun, Shuyan Chen, Longhan Zhang, Peijie Dong, Huajie You, Yongqi Zhang, Chang Yan, Xiaowen Chu, Tong-yi Zhang
| Challenge: | a rapid advancement of perovskite solar cells has led to an exponential growth in research publications. |
| Approach: | They propose a knowledge-enhanced system for perovskite solar cells that integrates three key components. |
| Outcome: | The proposed system outperforms existing models in domain-specific knowledge retrieval and scientific reasoning tasks. |
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation (2025.acl-long)
Copied to clipboard
Shuo Tang, Xianghe Pang, Zexi Liu, Bohan Tang, Rui Ye, Tian Jin, Xiaowen Dong, Yanfeng Wang, Siheng Chen
| Challenge: | Extensive experiments demonstrate that our framework effectively generates both general and domain-specific data. |
| Approach: | They propose a multi-agent simulator that automatically generates diverse text-based scenarios, capturing a wide range of real-world human needs. |
| Outcome: | Experiments show that the proposed model outperforms Meta’s Llama-3-8B-Instruct model on AlpacaEval 2 and Arena-Hard benchmarks with just 20K instruction-response pairs. |
LongGenBench: Long-context Generation Benchmark (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current long-context benchmarks focus on retrieval-based tests, requiring Large Language Models to locate specific information within extensive input contexts. |
| Approach: | They propose a long-context generation benchmark that allows for flexible configurations of customized generation context lengths. |
| Outcome: | The proposed benchmark improves performance on NIAH and other retrieval-based tests. |
Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Intrusion detection systems (IDS) are limited in labeled samples due to scarcity and lack of diversity in malicious samples. |
| Approach: | They propose a semi-supervised framework that integrates Generative Adversarial Networks with Large Language Models to enhance malicious code generation and SQL Injection detection capabilities. |
| Outcome: | The proposed framework enhances malicious code generation and detection capabilities in few-sample learning scenarios. |