Papers by Shanshan Liu
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)
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| Challenge: | Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation. |
| Approach: | They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain. |
| Outcome: | The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition. |
Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning (2025.coling-main)
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| Challenge: | Current knowledge graph models focus on embedding entities and relations, overlooking the broader structure of the entire knowledge graph. |
| Approach: | They propose a Temporal Knowledge Graph Reasoning model that embeds relation embeddings into the TKG. |
| Outcome: | The proposed model outperforms state-of-the-art models on five public datasets . it uses relation-aware attention mechanisms to learn relation embeddings based on query relations . |
Dissecting GraphRAG: A Modular Analysis of Knowledge Structuring for Factoid Question Answering (2026.tacl-1)
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Noriki Nishida, Rumana Ferdous Munne, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Fei Cheng, Kouji Kozaki, Yuji Matsumoto
| Challenge: | GraphRAG integrates structured knowledge graphs into question answering . high-quality triple extraction is critical, but lacks granularity and topical coherence . large language models suffer from inherent limitations in their internalized knowledge . |
| Approach: | They evaluate module-level design choices in GraphRAG for retrieval-augmented generation . they find that triple extraction is critical for accurate and comprehensive retrieval . |
| Outcome: | The proposed framework outperforms other retrieval-augmented generation frameworks in accuracy and efficiency. |
Zero-Shot Entailment Learning for Ontology-Based Biomedical Annotation Without Explicit Mentions (2025.coling-main)
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Rumana Ferdous Munne, Noriki Nishida, Shanshan Liu, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Automated biomedical annotation presents significant challenges when entities are not explicitly mentioned in the text. |
| Approach: | They propose an entailment-based zero-shot text classification approach to annotate biomedical text passages using the Homeostasis Imbalance Process (HOIP) ontology. |
| Outcome: | The proposed method performs well when processes are not explicitly mentioned . it is time-consuming and expensive to annotate biomedical texts with a specific ontology . |
MA-COIR: Leveraging Semantic Search Index and Generative Models for Ontology-Driven Biomedical Concept Recognition (2025.acl-srw)
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Shanshan Liu, Noriki Nishida, Rumana Ferdous Munne, Narumi Tokunaga, Yuki Yamagata, Kouji Kozaki, Yuji Matsumoto
| Challenge: | Existing concepts recognition methods that rely on explicit mention identification fail to capture complex concepts not explicitly stated in the text. |
| Approach: | They propose a framework that reformulates concept recognition as an indexing-recognition task. |
| Outcome: | The proposed framework reduces computational requirements and improves recognition efficiency in low-resource settings. |
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). |
| Approach: | They propose a benchmark to evaluate associative ability while circumventing the inherent ambiguity in association tasks by decomposing ambiguities into two types and propose 'assoCiAm' they conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. |
| Outcome: | The proposed method shows that ambiguity in association evaluations makes MLLMs more random-like and the model's behavior more random. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Why and How LLMs Benefit from Knowledge Introspection in Commonsense Reasoning (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) can improve commonsense reasoning by generating intermediate knowledge, but the effectiveness of this knowledge introspection is not always guaranteed. |
| Approach: | They propose a training-free strategy that optimizes introspection via two stages: Knowledge Detection and Knowledge Regeneration. |
| Outcome: | The proposed approach mitigates the limitations of standard introspection and has consistent performance gains across all settings. |
MONTROSE: LLM-driven Monte Carlo Tree Search Self-Refinement for Cross-Domain Rumor Detection (2025.findings-acl)
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| Challenge: | Existing feature alignment methods are susceptible to task interference during training. |
| Approach: | MONTROSE is a cross-domain rumor detection method that generates high-quality synthetic data for the target domain and a domain-sharpness-aware approach to train models with these synthetic data. |
| Outcome: | Experiments show that MONTROSE improves in cross-domain rumor detection. |
GATE: Graph-based Adaptive Tool Evolution Across Diverse Tasks (2026.acl-long)
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Jianwen Luo, Yiming Huang, Jinxiang Meng, Fangyu Lei, Shizhu He, Xiao Liu, Shanshan Jiang, Bin Dong, Jun Zhao, Kang Liu
| Challenge: | Existing toolsets that use large language models are limited to single-task settings. |
| Approach: | They propose a framework that dynamically constructs and evolves a hierarchical graph of reusable tools across multiple scenarios. |
| Outcome: | The proposed framework achieves up to 4.3 faster milestone completion in Minecraft compared to the previous state-of-the-art method and provides an average improvement of 9.23% over existing tool-making methods in code generation tasks and 10.03% in agent tasks. |
Better Generalizing to Unseen Concepts: An Evaluation Framework and An LLM-Based Auto-Labeled Pipeline for Biomedical Concept Recognition (2026.eacl-long)
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Shanshan Liu, Noriki Nishida, Fei Cheng, Narumi Tokunaga, Rumana Ferdous Munne, Yuki Yamagata, Kouji Kozaki, Takehito Utsuro, Yuji Matsumoto
| Challenge: | Existing methods for recognizing ontology concepts are limited by the number of annotations available. |
| Approach: | They propose an evaluation framework built on hierarchical concept indices and novel metrics to measure generalization. |
| Outcome: | The proposed evaluation framework is built on hierarchical concept indices and novel metrics to measure generalization. |
Supervised neural machine translation based on data augmentation and improved training & inference process (D19-52)
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| Challenge: | This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019 . |
| Approach: | They propose a model for translation tasks of WAT 2019 that employs a Transformer model as the baseline and a deep layer model to improve translation quality. |
| Outcome: | The proposed methods can improve translation quality over traditional statistical machine translation (SMT) The proposed models can improve the translation quality of Japanese-English and Japanese-Chinese corpus. |