Papers by Jiajia Li
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)
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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao
| Challenge: | Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. |
| Approach: | They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison. |
| Outcome: | The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research. |
NOTA: Multimodal Music Notation Understanding for Visual Large Language Model (2025.findings-naacl)
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| Challenge: | Existing general-domain visual language models lack ability of music notation understanding . Symbolic music is represented in two distinct forms: auditory music and symbolic music . |
| Approach: | They propose to train a multimodal music notation model using a large-scale dataset . they use cross-modal alignment to train the model for music notations analysis . |
| Outcome: | The proposed model improves on music understanding by training with a multimodal music notation model. |
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network (2021.acl-long)
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| Challenge: | Existing methods for multimodal sentiment analysis require all modalities as input, thus are sensitive to missing modality at predicting time. |
| Approach: | They propose to model bi-direction interplay via couple learning and exploit multiple bi-directional translations to exploit multimodal fusion embeddings. |
| Outcome: | The proposed framework achieves state-of-the-art or often competitive performance on two multimodal benchmarks with extensive ablation studies. |
BoYaEval: Evaluating Multimodal Large Language Models on Understanding Ancient Chinese Musical Scores (2026.acl-long)
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| Challenge: | Multimodal Large Language Models excel in general tasks but struggle with specialized, structured cultural symbols. |
| Approach: | They evaluate 21 leading MLLMs and compare their performance to a benchmark for Ancient Chinese musical notation. |
| Outcome: | The benchmark evaluates 21 leading MLLMs on five types of ancient Chinese music notation systems. |
The Music Maestro or The Musically Challenged, A Massive Music Evaluation Benchmark for Large Language Models (2024.findings-acl)
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| Challenge: | Existing benchmarks to evaluate LLMs' capabilities are inadequate for assessing their musical capabilities. |
| Approach: | They propose to use a large-scale music benchmark specifically designed to evaluate the music-related capabilities of large language models (LLMs). |
| Outcome: | The proposed framework evaluates 16 large language models in the domain of music. |
Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction (2025.naacl-long)
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| Challenge: | Extractive UIEs can solve model explosion problems using a relatively small model . single-target instruction UIE enables the extraction of only one type of relation at a time . |
| Approach: | They propose a model that assigns different relations to different levels for understanding and decision-making. |
| Outcome: | Experiments show that LDNet outperforms state-of-the-art systems on 9 tasks, 33 datasets . LDnet outperformed state- of-the art systems on single-modal and multi-modal tasks . |