Papers by Xiangyang Luo
Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)
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| Challenge: | Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability. |
| Approach: | They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes. |
| Outcome: | The proposed framework achieves remarkable controllability while keeping the text fluent and diverse. |
AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)
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Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng
| Challenge: | Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content. |
| Approach: | They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories . |
| Outcome: | The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources. |
Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-main)
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| Challenge: | Existing image captioning models require large trainable parameters to bridge visual and textual representations. |
| Approach: | They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model. |
| Outcome: | The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models. |
Mitigating Language Confusion through Inference-time Intervention (2025.coling-main)
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| Challenge: | Existing methods to address the problem of language confusion are incontext learning and supervised fine-tuning (SFT) however, they consume context window space and require extensive data collection. |
| Approach: | They propose a language-sensitive intervention that detects and assesses language confusion without additional complex mechanisms. |
| Outcome: | The proposed method detects language confusion and assesses content quality without additional complex mechanisms. |
Efficient Sparse Attention needs Adaptive Token Release (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks, however, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. |
| Approach: | They propose to release resources from caches and rebuild key-value states by a lightweight controller module to approximate an ideal top-K sparse attention. |
| Outcome: | The proposed method achieves a significant throughput improvement of 221.8% over full attention and a model with 7 billion tokens. |
LightMoE: Task-Aware Expert Availability Management for Memory-Efficient MoE-LLM Inference (2026.findings-acl)
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| Challenge: | Existing solutions for balancing model accuracy with inference latency are limited due to memory constraints. |
| Approach: | They propose a framework for memory-efficient MoE inference that exploits the functional redundancy and temporal locality of expert activation. |
| Outcome: | The proposed framework improves accuracy-efficiency trade-off by 4.3% over pruned models and 2.4% over dynamic swapping methods while maintaining inference latency comparable to pruned model. |