Papers by Frank Wang
Coloring the Blank Slate: Pre-training Imparts a Hierarchical Inductive Bias to Sequence-to-sequence Models (2022.findings-acl)
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| Challenge: | Sequence-to-sequence models fail to generalize in hierarchy-sensitive manner when performing syntactic transformations. |
| Approach: | They evaluate whether seq2seq models generalize hierarchically on two transformations . they use pre-trained models and their multilingual variants to test their generalization . |
| Outcome: | The proposed models generalize hierarchically on two transformations in English and German. |
A Survey on LLMs for Story Generation (2025.findings-emnlp)
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Maria Teleki, Vedangi Bengali, Xiangjue Dong, Sai Tejas Janjur, Haoran Liu, Tian Liu, Cong Wang, Ting Liu, Yin Zhang, Frank Shipman, James Caverlee
| Challenge: | Methods for story generation with Large Language Models (LLMs) have come into the spotlight recently. |
| Approach: | They propose a novel taxonomy of LLMs for story generation consisting of two major paradigms: independent story generation by an LLM, and author-assistance for story creation . |
| Outcome: | The proposed taxonomy compares existing work on the topic with those of novel author-assistance models. |
Graph-tree Fusion Model with Bidirectional Information Propagation for Long Document Classification (2024.findings-emnlp)
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| Challenge: | Existing methods for document classification struggle with token limits and fail to adequately model hierarchical relationships within documents. |
| Approach: | They propose a novel model leveraging a graph-tree structure to capture local and global dependencies. |
| Outcome: | The proposed model captures syntactic relationships and broader document contexts without token limits and can handle arbitrarily long contexts. |
Multi-Step Generation of Test Specifications using Large Language Models for System-Level Requirements (2025.acl-industry)
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| Challenge: | System-level testing is a critical phase in the development of large, safety-dependent systems, such as those in the automotive industry. |
| Approach: | They propose an AI-powered assistant to aid users in creating test specifications for system-level requirements. |
| Outcome: | The proposed system reduces the effort required to derive test specifications by 30% in ROUGE-L. |
LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection (2025.acl-industry)
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| Challenge: | e-commerce payment fraud detection is a new area for reinforcement learning (RL) and Large Language Models (LLMs). |
| Approach: | They propose to integrate reinforcement learning (RL) with Large Language Models (LLMs) by framing transaction risk as a multi-step Markov Decision Process (MDP), RL optimizes risk detection across multiple payment stages. |
| Outcome: | The proposed approach improves fraud detection accuracy and demonstrates zero-shot capability. |
Metaphor Detection via Explicit Basic Meanings Modelling (2023.acl-short)
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| Challenge: | Existing methods for metaphor detection use the aggregated meaning of a word to approximate its basic meaning. |
| Approach: | They propose a method which models the basic meaning of a word based on literal annotations and compares this with the contextual meaning in a target sentence to identify metaphors. |
| Outcome: | The proposed method outperforms the state-of-the-art method significantly in the F1 score and even reaches the theoretical upper bound on the VUA18 benchmark. |
Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation (2024.naacl-long)
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| Challenge: | Existing methods for ICD indexing have a heavy label distribution and a manual process . Xie and Xing (2017) propose a new approach to ICD re-ranking . |
| Approach: | They propose a "retrieve and re-rank" framework to allocate subsets of ICD codes to medical records . they leverage auxiliary knowledge of the electronic health records (EHR) and a discrete retrieval method . |
| Outcome: | The proposed method achieves state-of-the-art performance on the MIMIC-III benchmark. |
On the data requirements of probing (2022.findings-acl)
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| Challenge: | Existing methods to probe neural networks are expensive and require large datasets. |
| Approach: | They propose a method to estimate the required number of data samples in probing datasets . they use a classification task to encode a text with a deep neural network . |
| Outcome: | The proposed method estimates the required number of data samples in two probing configurations and proves it is statistically powerful. |
Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification (2024.lrec-main)
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| Challenge: | Existing methods for ICD indexing use machine learning to assign subset of codes to medical records . experimental results show proposed method achieves state-of-the-art performance on a number of measures. |
| Approach: | They propose a method that uses a deep dilated residual convolution encoder to learn document representations across different lengths of the texts. |
| Outcome: | The proposed method achieves state-of-the-art performance on a number of measures. |
Dynamic Generation of Multi LLM Agents Communication Topologies with Graph Diffusion Models (2026.acl-long)
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Eric Hanchen Jiang, Levina Li, Frank Wan, Xiao Liang, Sophia Yin, Yuchen Wu, Xinfeng Li, Yizhou Sun, Wei Wang, Kai-Wei Chang, Ying Nian Wu
| Challenge: | Existing frameworks rely on static or rule-based topologies that fail to adapt to task requirements. |
| Approach: | They propose a generative framework that generates highly task-adaptive topologies . they validated the framework on multiple benchmarks and validated it on multiple platforms . |
| Outcome: | The proposed framework outperforms existing frameworks in task-adaptive communication topologies. |
MeSHup: Corpus for Full Text Biomedical Document Indexing (2022.lrec-1)
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| Challenge: | Medical Subject Heading (MeSH) indexing is a problem of assigning a given biomedical document with the most relevant labels from an extremely large set of MeSH terms. |
| Approach: | They train an end-to-end model that combines features from documents and associated labels on MEDLINE corpus and report the new baseline. |
| Outcome: | The proposed system can be used to assign a biomedical document with the most relevant labels from an extremely large set of MeSH terms. |
Metaphor Detection with Effective Context Denoising (2023.eacl-main)
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| Challenge: | Existing models focus on semantically relevant information and provide a target-oriented parse tree structure for metaphor detection. |
| Approach: | They propose a new model which introduces a target-oriented parse tree structure for metaphor detection. |
| Outcome: | The proposed model achieves state-of-the-art on several main metaphor datasets and compares with other methods. |
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (2023.eacl-main)
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| Challenge: | Existing models for concept-level metaphor detection lack explicit knowledge of FrameNet . Metaphor detection is a pervasive linguistic device that is used in cognitive and communicative functions of language. |
| Approach: | They propose a BERT-based model that explicitly learns FrameNet Embeddings for metaphor detection. |
| Outcome: | The proposed model is more explainable and interpretable than existing models. |
KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling (2022.acl-long)
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| Challenge: | Medical Subject Headings (MeSH) are manually assigned to every biomedical article to facilitate retrieval of relevant information. |
| Approach: | They propose a model that combines new text features with a dynamic knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. |
| Outcome: | The proposed model achieves state-of-the-art on a number of measures. |
Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025.emnlp-main)
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Yinuo Wang, Baiyang Wang, Robert Mercer, Frank Rudzicz, Sudipta Singha Roy, Pengjie Ren, Zhumin Chen, Xindi Wang
| Challenge: | achieving comprehensive trustworthiness in medical QA poses significant challenges due to complexity of healthcare data, critical nature of clinical scenarios, and multifaceted dimensions of trustworthy AI. |
| Approach: | They examine six key dimensions of trustworthiness in medical QA . they compare how each dimension is evaluated in existing LLM-based systems . |
| Outcome: | The findings show that large language models have improved patient safety and effectiveness . the models exhibit critical trust failures when deployed in clinical settings . |
SlackAgents: Scalable Collaboration of AI Agents in Workspaces (2025.emnlp-demos)
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Zhiwei Liu, Weiran Yao, Zuxin Liu, Juntao Tan, Jianguo Zhang, Frank Wang, Sukhandeep Nahal, Huan Wang, Shelby Heinecke, Silvio Savarese, Caiming Xiong
| Challenge: | Existing open-source frameworks like LangChain and LlamaIndex fail to integrate into daily workflows, resulting in limited daily usage for work. |
| Approach: | They propose a multi-agent library for scalable management and collaboration of AI agents on Slack. |
| Outcome: | The proposed framework offers instant AI integration into organizational workflows and facilitates scalable collaboration, allowing for effective communication and task orchestration. |