Papers by Xian-Sheng Hua
SDBench: A Survey-based Domain-specific LLM Benchmarking and Optimization Framework (2025.acl-long)
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| Challenge: | acquiring domain-specific knowledge often requires professional expert manpower. |
| Approach: | They propose a generic framework for generating evaluation datasets for domain-specific LLMs. |
| Outcome: | The proposed framework reduces the reliance on expert manpower while ensuring that the collected data is uniformly distributed. |
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. |
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. |
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. |