Papers by Xiaobing Li
CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages (2025.findings-acl)
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Shangda Wu, Guo Zhancheng, Ruibin Yuan, Junyan Jiang, SeungHeon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun
| Challenge: | Music information retrieval (MIR) is a field that aims at developing computational tools for processing, organizing, and accessing music data. |
| Approach: | They propose a framework that aligns music modalities with multilingual text in a shared representation space. |
| Outcome: | Experiments show CLaMP 3 performs state-of-the-art on multiple MIR tasks . it surpasses baselines and shows excellent generalization in multimodal and multilingual contexts . |
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction (D19-57)
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| Challenge: | BioNLP 2019 Shared Tasks: binary relation extraction of SeeDev task . Biological information extraction (Bio-IE) is a new field of research . |
| Approach: | They propose to use convolutional neural networks and long short term memory networks to construct a binary relation extraction model. |
| Outcome: | The proposed method performed well in the binary relation extraction task. |
Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)
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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun
| Challenge: | Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores. |
| Approach: | They propose a benchmark for score-level musical understanding across textual and visual modalities. |
| Outcome: | The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others. |
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)
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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun
| Challenge: | Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages . |
| Approach: | They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder . |
| Outcome: | The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities. |
Unraveling Feature Extraction Mechanisms in Neural Networks (2023.emnlp-main)
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| Challenge: | Neural networks have become indispensable across a variety of natural language processing tasks. |
| Approach: | They propose a theoretical approach based on Neural Tangent Kernels to investigate neural networks' internal mechanisms. |
| Outcome: | The proposed approach can be applied to analyze language modeling tasks . it shows that the choice of activation function can affect feature extraction . |
Lingxi: A Diversity-aware Chinese Modern Poetry Generation System (2023.acl-demo)
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| Challenge: | Chinese modern poetry generation is a challenging task because of the word segmentation problem and decoding methods . the decoding method may induce repetition and boredom and lower the diversity of generated poetry. |
| Approach: | They propose a Chinese word segmentation-based decoding system that incorporates Chinese word segments into tokenization. |
| Outcome: | The proposed system can achieve high vocabulary coverage rate with a reasonable vocabulary size. |
Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling (2025.emnlp-main)
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| Challenge: | Experimental results show superior cross-model transferability . Prompt injection attacks are among the most critical threats . |
| Approach: | They propose an activations-guided prompt injection attack framework to address the impracticality of existing white-box/gray-box methods and the poor transferability of black-box approaches. |
| Outcome: | The proposed framework achieves 49.6% success rate and 34.6% improvement over human-crafted prompts on five mainstream LLMs. |