Papers by Rex Ying
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
Mixture-of-Personas Language Models for Population Simulation (2025.findings-acl)
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Ngoc Bui, Hieu Trung Nguyen, Shantanu Kumar, Julian Theodore, Weikang Qiu, Viet Anh Nguyen, Rex Ying
| Challenge: | Pretrained LLMs fail to capture behavioral diversity of target populations due to inherent variability across individuals and groups. |
| Approach: | They propose a probabilistic prompting method that aligns LLM responses with the target population. |
| Outcome: | Experiments show that the proposed method outperforms competing methods in alignment and diversity metrics. |
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains (2024.findings-emnlp)
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Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan
| Challenge: | Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales. |
| Approach: | They propose to annotate a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans. |
| Outcome: | The proposed model outperforms existing methods on understanding the capabilities of LLMs in logical reasoning by 10% or more. |
GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models (2025.findings-emnlp)
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Jialin Chen, Houyu Zhang, Seongjun Yun, Alejandro Mottini, Rex Ying, Xiang Song, Vassilis N. Ioannidis, Zheng Li, Qingjun Cui
| Challenge: | Existing graph RAGs decouple retrieval and reasoning processes, preventing adaptability . existing graph Raggings depend heavily on ground-truth entities, which are often unavailable in open-domain settings. |
| Approach: | They propose a graph retriever that is trained end-to-end with large-scale graphs . structure and semantic features are encoded via soft tokens and the verbalized graph . |
| Outcome: | The proposed approach improves the performance of large-scale graph retrieval models by grounding it with external knowledge. |
Graph Ensemble Learning over Multiple Dependency Trees for Aspect-level Sentiment Classification (2021.naacl-main)
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| Challenge: | Recent work on aspect-level sentiment classification has shown that syntactic information is effective in capturing long-range syntaktic relations that are obscure from the surface form. |
| Approach: | They propose a graph ensemble technique that integrates syntactic structures with GNNs to better leverage syntaktic information in the face of parsing errors. |
| Outcome: | The proposed model outperforms models with single dependency tree and beats other models without adding model parameters. |
Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA (2026.acl-long)
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| Challenge: | Existing methods to improve the reliability of Large Language Models (LLMs) in clinical applications require factual knowledge from open-ended datasets and clinical case-based knowledge to provide context grounded in real-world patient experiences. |
| Approach: | They propose a retrieval-augmented generation framework based on the electronic health record to offer contextual information from other patients’ discharge reports. |
| Outcome: | The proposed framework outperforms a text-based ranker in a clinical QA dataset with 1,280 discharge-related questions . |
HiPool: Modeling Long Documents Using Graph Neural Networks (2023.acl-short)
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| Challenge: | Recent work on pretraining languages have achieved satisfying results in many NLP tasks, but they are still restricted by a pre-defined maximum length. |
| Approach: | They propose a graph-based method to model sentence-level information using a fixed length and graphs to model intra- and cross-sentence correlations. |
| Outcome: | The proposed model outperforms baseline models by 2.6% in F1 score, and 4.8% on the longest sequence dataset. |
Long Sequence Modeling with Attention Tensorization: From Sequence to Tensor Learning (2024.findings-emnlp)
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| Challenge: | a lack of attention-based models for long sequences poses challenges for long-sequence modeling . attention tensorization can be used to extend context lengths with improved efficiency . tenorization enables training of LLMs with context length longer than those trained on . |
| Approach: | They propose to tensorize long input sequences into compact tenses followed by attention on each transformed dimension. |
| Outcome: | The proposed model can be used as efficient transformer backbones to extend input context length with improved memory and time efficiency. |