Papers by Xiaobing Zhou

4 papers
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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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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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.
Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling (2024.lrec-main)

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Challenge: Chain of thought (CoT) is used for complex reasoning problems, but hallucinations are a problem in multimodal CoT.
Approach: They propose a method to generate soft negative samples with different semantics to mitigate hallucinations in multimodal CoT.
Outcome: The proposed method mitigates hallucinations in multimodal CoT by using soft negative sampling.

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