Papers by Xiran Fan
Enhancing Foundation Models in Transaction Understanding with LLM-based Sentence Embeddings (2025.emnlp-industry)
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| Challenge: | Existing foundation models for tabular transactional data rely on index-based representations for categorical merchant fields. |
| Approach: | They propose a framework that uses LLM-generated embeddings as semantic initializations for lightweight transaction models. |
| Outcome: | The proposed framework improves performance on large transaction datasets. |
Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)
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Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu
| Challenge: | Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights. |
| Approach: | They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback. |
| Outcome: | The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience. |
Cross-lingual Social Misinformation Detector based on Hierarchical Mixture-of-Experts Adapter (2025.coling-main)
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| Challenge: | a global trend of misinformation is affecting non-native speaker users who are more susceptible to misinformation on foreign social media platforms. |
| Approach: | They propose a method to integrate sentiment analysis as an auxiliary task and a hierarchical routing strategy and expert-mask mechanism to enhance cross-lingual social misinformation detection. |
| Outcome: | The proposed method improves cross-lingual social misinformation detection in non-native speakers with only monolingual social media histories. |
Enhancing Hyperbolic Knowledge Graph Embeddings via Lorentz Transformations (2024.findings-acl)
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| Challenge: | Existing methods for knowledge graph embedding rely on tangent approximation and are not fully hyperbolic. |
| Approach: | They propose a fully hyperbolic KGE method that represents entities as points in the Lorentz model and represents relations as the intrinsic transformation. |
| Outcome: | The proposed method captures various types of relations including hierarchical structures. |