Papers by Chong Luo
DEMO: A Statistical Perspective for Efficient Image-Text Matching (2024.naacl-long)
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| Challenge: | Image-text matching is a problem that seeks to connect vision and language through semantic understanding. |
| Approach: | They propose a deep unsupervised hashing-based approach for image-text matching . they characterize each image using multiple augmented views, which are considered as samples . |
| Outcome: | The proposed approach achieves superior performance on image-text matching datasets compared with state-of-the-art methods. |
GALA: Geometric Data Selection with Strategic Prospecting for Large Language Model Self-training (2026.findings-acl)
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| Challenge: | Existing approaches to self-training are based on reject sampling and lack quality reasoning paths. |
| Approach: | They propose a framework for self-training using a generate-and-filter paradigm . they propose to identify diverse and informative samples from redundant data and exploit them more strategically. |
| Outcome: | The proposed framework exploits informative samples from redundant data and improves reasoning trajectory prospecting. |
A Context-based Framework for Modeling the Role and Function of On-line Resource Citations in Scientific Literature (D19-1)
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| Challenge: | Existing academic search engines cannot detect relevant papers where a resource is mentioned. |
| Approach: | They propose a framework to model the role and function of on-line resource citations . they construct a dataset SciRes, which includes 3,088 manually annotated resource contexts based on a multi-task framework . |
| Outcome: | The proposed model achieves the best results on both the classification task and recommendation task. |
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)
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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs. |
| Approach: | They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them. |
| Outcome: | The proposed architecture achieves comparable performance with GShard with 2B parameters and computation. |
LEAF: Large Language Diffusion Model for Time Series Forecasting (2025.findings-emnlp)
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| Challenge: | Recent work has applied large language models (LLMs) into time series forecasting, but they lack an understanding of holistic temporal patterns with potential error accumulation. |
| Approach: | They propose a framework that marries Larg e Langu age Diffusion Model with time series forecasting (LEAF) they propose converting time series into tokens and adopting language diffusion models to capture temporal dependencies. |
| Outcome: | The proposed framework generates future predictions with a diffusion model from a holistic view. |
Shorten After You’re Right: Lazy Length Penalties for Reasoning RL (2026.findings-acl)
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Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, Dongyan Zhao
| Challenge: | Existing shortening methods for long reasoning models rely on additional supervision or multi-stage post-training. |
| Approach: | They propose a lazy length penalty that imposes length pressure on models without extra training stages. |
| Outcome: | The proposed method significantly reduces response length without extra training stages while maintaining or improving performance. |
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2025.acl-long)
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Jingyang Yuan, Huazuo Gao, Damai Dai, Junyu Luo, Liang Zhao, Zhengyan Zhang, Zhenda Xie, Yuxing Wei, Lean Wang, Zhiping Xiao, Yuqing Wang, Chong Ruan, Ming Zhang, Wenfeng Liang, Wangding Zeng
| Challenge: | Long-context modeling is crucial for next-generation language models, but high computational cost of standard attention mechanisms poses significant computational challenges. |
| Approach: | They propose a natively trained Sparse Attention mechanism that integrates algorithms with hardware-aligned optimizations to achieve efficient long-context modeling. |
| Outcome: | The proposed model maintains or exceeds Full Attention models across general benchmarks, long-context tasks, and instruction-based reasoning. |
Dynamic Emotion and Personality Profiling for Multimodal Deception Detection (2026.acl-long)
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| Challenge: | Existing methods for deception detection lack sample-level dynamic annotations for emotions and personality. |
| Approach: | They propose a multi-model multi-prompt annotation scheme and a strict label quality evaluation standard for deception, emotion, and personality annotations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the MDPE and DDEP datasets. |
scRAG: Hybrid Retrieval-Augmented Generation for LLM-based Cross-Tissue Single-Cell Annotation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated impressive potential in a wide range of fields, including biology, genomics and healthcare. |
| Approach: | They propose a framework that integrates advanced LLM-based RAG techniques into cross-tissue single-cell annotation. |
| Outcome: | The proposed framework outperforms baseline models, generalist models, domain-specific methods, and trained classifiers on a cross-tissue dataset. |