Papers by Ying Tan
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature (2024.findings-emnlp)
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Dawei Li, Shu Yang, Zhen Tan, Jae Baik, Sukwon Yun, Joseph Lee, Aaron Chacko, Bojian Hou, Duy Duong-Tran, Ying Ding, Huan Liu, Li Shen, Tianlong Chen
| Challenge: | Recent advances in large language models have achieved promising performances across various applications, but the challenge of integrating long-tail knowledge continues to impede the seamless adoption of LLMs in specialized domains. |
| Approach: | They propose a dynamic co-augmentation framework for the refinement of large language models and knowledge graphs in the context of Alzheimer's Disease. |
| Outcome: | The proposed framework can be used to study Alzheimer's Disease (AD) using LLMs and KGs. |
LiGen: Active Lipid Generation via a Molecular Language Model (2026.acl-long)
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| Challenge: | Lipid nanoparticles (LNPs) can deliver cargos to tumor and immune cells . traditional approaches rely on experimental screening and expert judgment . |
| Approach: | They propose a method to generate lipid molecules efficiently and actively using deep learning. |
| Outcome: | The proposed method outperforms baseline methods on multiple cell lines and achieves a 30% improvement over the current methods. |
Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization (2024.findings-emnlp)
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| Challenge: | Abstractive text summarization (ATS) requires laborious data annotation and time-consuming model training. |
| Approach: | They propose a novel active learning framework that asks large language models to rate difficulty of instances and then uses certainty gain maximization to select instances with a distribution that aligns well with the overall distribution. |
| Outcome: | The proposed framework improves stability, effectiveness, and efficiency of abstractive text summarization backbones. |
CrystalICL: Enabling In-Context Learning for Crystal Generation (2025.emnlp-main)
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| Challenge: | Existing methods for crystal generation are limited to zero-shot scenarios and are unable to benefit from few-shot situations. |
| Approach: | They propose a model designed for few-shot crystal generation that exploits in-context learning by capturing structure-property relationships from limited data. |
| Outcome: | The proposed model reduces complexity of modeling crystal symmetry in LLMs and exploits ICL by capturing structure-property relationships from limited data. |
Language-Informed Synthesis of Rational Agent Models for Grounded Theory-of-Mind Reasoning On-the-fly (2025.findings-emnlp)
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Lance Ying, Ryan Truong, Katherine M. Collins, Cedegao E. Zhang, Megan Wei, Tyler BrookeWilson, Tan Zhi-Xuan, Lionel Wong, Joshua B. Tenenbaum
| Challenge: | Language is a powerful source of information in social settings, especially in novel situations where language can provide both abstract information about the environment dynamics and concrete specifics about an agent that cannot be easily visually observed. |
| Approach: | They propose a language-informed rational agent synthesis framework that integrates linguistic and visual inputs to draw context-specific social inferences. |
| Outcome: | The proposed framework outperforms ablations and state-of-the-art models on a range of social reasoning tasks derived from cognitive science experiments. |
Hi-ZFO: Hierarchical Zeroth- and First-Order LLM Fine-Tuning via Importance-Guided Tensor Selection (2026.findings-acl)
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| Challenge: | generative tasks require a high degree of exploratory capacity, but zeroth-order methods suffer from slow convergence . generative task-specific methods tend to converge toward local minima, causing noise and inefficient estimation . |
| Approach: | They propose a framework that synergizes FO precision with exploratory capability of ZO estimation. |
| Outcome: | The proposed framework synergizes precision of FO gradients with exploratory capability of ZO estimation. |
CompTab: A Comprehensive Benchmark for Real-World TableQA with Complex Reasoning and Irregular Tables (2026.acl-long)
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Zhen Yang, Wei Du, Jie Wang, Wenze Zhou, Xiangfeng Meng, Zhengyang Wang, Suping Sun, Ziwei Du, Haodong Zou, Jie Chen, Yongbin Liu, Shicheng Tan, Jiahao Ying, Shu Zhao
| Challenge: | Existing benchmarks focus on well-structured tables and fail to reflect irregular structures and complex reasoning commonly encountered in real-world scenarios. |
| Approach: | They propose a benchmark to evaluate TableQA under complex reasoning and irregular table conditions. |
| Outcome: | The proposed framework improves generalization and realism of large language models under complex and irregular table conditions. |
Neural-DINF: A Neural Network based Framework for Measuring Document Influence (2020.acl-main)
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| Challenge: | Existing methods to measure scholarly impact of documents without citations only consider word frequency change. |
| Approach: | They propose a neural network framework that measures document influence without citations by using word frequency changes and word semantic shifts. |
| Outcome: | The proposed model outperforms existing models on document influence evaluation without citations. |
Latent-Condensed Transformer for Efficient Long Context Modeling (2026.acl-long)
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| Challenge: | Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. |
| Approach: | They propose a Latent-Condensed Attention mechanism that performs structured context condensation directly within MLA's latent space. |
| Outcome: | The proposed approach reduces KV cache size and attention cost without adding parameters. |