2024

1. In Submission

   RECOVER: Designing a Large Language Model-based Remote Patient Monitoring System for Postoperative GI Cancer Care

   Ziqi Yang*, Yuxuan Lu*, Jennifer Bagdasarian, Vedant Das Swain, Ritu Agarwal, Collin Campbell, Waddah Al-Refaire, Dr Jehan El-Bayoumi, Guodong (Gordon) Gao,  shara, Dakuo Wang, and Bingsheng Yao

   In Submission to CHI 2025, 2024
2. In Submission

   UXAgent: An Large Language Model-based Agent System for Usability Testing of Web Design

   Yuxuan Lu, Arthur Yao, Hansu Gu, Jing Huang, Jessie Wang, Laurence Li, Haiyang Zhang, Qi He, Toby Jia-Jun Li, and Dakuo Wang

   In Submission to CHI 2025, 2024
3. In Submission

   Characterizing LLM-Empowered Personalized Story Reading and Interaction for Children: Insights From Multi-Stakeholders’ Perspective

   Jiaju Chen, Minglong Tang, Yuxuan Lu, Bingsheng Yao, Elissa Fan, Xiaojuan Ma, Ying Xu, Dakuo Wang, Yuling Sun, and Liang He

   In Submission to CHI 2025, 2024
4. In Submission

   From Dark Data to Open Data: Challenges and Practices for Data Integrators of Data-Driven Open Science Projects in Geoscience

   Shao Zhang, Shihan Fu, Bin Lu, Yuxuan Lu, Toby Jia-Jun Li, Dakuo Wang, Ying Wen, Xinbing Wang, and Chenghu Zhou

   In Submission to CSCW 2025, 2024
5. In Submission

   Exploring Domain Adaptation with LLMs for Real-World Augmented Question Answer Generation (RA-QAG) in Children Storytelling

   Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, and Yuling Sun

   In Submission to EMNLP 2024, 2024
6. In Submission

   ALERTS: Active Learning and Ensemble LLM Real-Time Switch for Real-World Data Drift Challenges

   Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yisi Sang, Yun Wang, Hansu Gu, Peng Zhang, Tun Lu, Toby Jia-Jun Li, and Dakuo Wang

   In Submission to EMNLP 2024, 2024

2023

1. arXiv

   Human Still Wins over LLM: An Empirical Study of Active Learning on Domain-Specific Annotation Tasks

   Yuxuan Lu, Bingsheng Yao, Shao Zhang, Yun Wang, Peng Zhang, Tun Lu, Toby Jia-Jun Li, and Dakuo Wang

   arXiv preprint arXiv:2311.09825, 2023

   Abs arXiv PDF

   Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domain knowledge is often required. Low-resource learning methods like Active Learning (AL) have been proposed to tackle the cost of domain expert annotation, raising this question: Can LLMs surpass compact models trained with expert annotations in domain-specific tasks? In this work, we conduct an empirical experiment on four datasets from three different domains comparing SOTA LLMs with small models trained on expert annotations with AL. We found that small models can outperform GPT-3.5 with a few hundreds of labeled data, and they achieve higher or similar performance with GPT-4 despite that they are hundreds time smaller. Based on these findings, we posit that LLM predictions can be used as a warmup method in real-world applications and human experts remain indispensable in tasks involving data annotation driven by domain-specific knowledge.

2024

1. NAACL 2024

   More Samples or More Prompt Inputs? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering

   Bingsheng Yao, Guiming Chen, Ruishi Zou, Yuxuan Lu, Jiachen Li, Shao Zhang, Sijia Liu, James Hendler, and Dakuo Wang

   In Findings of the Association for Computational Linguistics: NAACL 2024, 2024

   Abs arXiv PDF

   While most existing works on LLM prompt-engineering focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can’t we design and leverage multiple prompt inputs together to further improve the LLM performance? In this work, we propose In-Context Sampling (ICS), a low-resource LLM prompt-engineering technique to produce the most confident prediction results by optimizing the construction of multiple ICL prompt inputs. Extensive experiments with two SOTA LLMs (FlanT5-XL and Mistral-7B) on three NLI datasets (e-SNLI, Multi-NLI, and ANLI) illustrate that ICS can consistently enhance LLM’s prediction performance and confidence. An ablation study suggests that a diversity-based ICS strategy may further improve LLM’s performance, which sheds light on a new yet promising future research direction.

2. WSDM 2024

   Professional Network Matters: Connections Empower Person-Job Fit

   Hao Chen, Lun Du, Yuxuan Lu, Qiang Fu, Xu Chen, Shi Han, Yanbin Kang, Guangming Lu, and Zi Li

   In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

   Abs

   Online recruitment platforms typically employ Person-Job Fit models in the core service that automatically match suitable job seekers with appropriate job positions. While existing works leverage historical or contextual information, they often disregard a crucial aspect: job seekers’ social relationships in professional networks. This paper emphasizes the importance of incorporating professional networks into the Person-Job Fit model. Our innovative approach consists of two stages: (1) defining a Workplace Heterogeneous Information Network (WHIN) to capture heterogeneous knowledge, including professional connections and pre-training representations of various entities using a heterogeneous graph neural network; (2) designing a Contextual Social Attention Graph Neural Network (CSAGNN) that supplements users’ missing information with professional connections’ contextual information. We introduce a job-specific attention mechanism in CSAGNN to handle noisy professional networks, leveraging pre-trained entity representations from WHIN. We demonstrate the effectiveness of our approach through experimental evaluations conducted across three real-world recruitment datasets from LinkedIn, showing superior performance compared to baseline models.

3. CSCW 2024

   Exploring Parent’s Needs for Children-Centered AI to Support Preschoolers’ Storytelling and Reading Activities

   Yuling Sun, Jiali Liu, Bingsheng Yao, Jiaju Chen, Dakuo Wang, Xiaojuan Ma, Yuxuan Lu, Ying Xu, and Liang He

   Proc. ACM Hum.-Comput. Interact., 2024

   Abs arXiv

   Interactive storytelling is vital for preschooler development. While children’s interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling technologies. As these technologies become increasingly ubiquitous in preschoolers’ lives, questions arise regarding how they function in practical storytelling scenarios and, in particular, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling technologies provide more immersive and engaging interaction, they still cannot meet parents’ expectations due to a series of interactive, functional, and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based storytelling technologies for preschoolers. We conclude by highlighting the design implications for future AI-based storytelling technologies.

4. CHI 2024

   Rethinking Human-AI Collaboration in Complex Medical Decision Making: A Case Study in Sepsis Diagnosis

   Shao Zhang, Jianing Yu, Xuhai Xu, Changchang Yin, Yuxuan Lu, Bingsheng Yao, Melanie Tory, Lace M. Padilla, Jeffrey Caterino, Ping Zhang, and Dakuo Wang

   In Proceedings of the CHI Conference on Human Factors in Computing Systems, 2024

   Abs arXiv PDF

   Today’s AI systems for medical decision support often succeed on benchmark datasets in research papers but fail in real-world deployment. This work focuses on the decision making of sepsis, an acute life-threatening systematic infection that requires an early diagnosis with high uncertainty from the clinician. Our aim is to explore the design requirements for AI systems that can support clinical experts in making better decisions for the early diagnosis of sepsis. The study begins with a formative study investigating why clinical experts abandon an existing AI-powered Sepsis predictive module in their electrical health record (EHR) system. We argue that a human-centered AI system needs to support human experts in the intermediate stages of a medical decision-making process (e.g., generating hypotheses or gathering data), instead of focusing only on the final decision. Therefore, we build SepsisLab based on a state-of-the-art AI algorithm and extend it to predict the future projection of sepsis development, visualize the prediction uncertainty, and propose actionable suggestions (i.e., which additional laboratory tests can be collected) to reduce such uncertainty. Through heuristic evaluation with six clinicians using our prototype system, we demonstrate that SepsisLab enables a promising human-AI collaboration paradigm for the future of AI-assisted sepsis diagnosis and other high-stakes medical decision making.

5. EMNLP 2024

   StorySpark: Expert-Annotated QA Pairs with Real-World Knowledge for Children Storytelling

   Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, and Yuling Sun

   In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

   Abs arXiv PDF

   Interactive story reading is a common parent-child activity, where parents expect to teach both language skills and real-world knowledge beyond the story. While increasing storytelling and reading systems have been developed for this activity, they often fail to infuse real-world knowledge into the conversation. This limitation can be attributed to the existing question-answering (QA) datasets used for children’s education, upon which the systems are built, failing to capture the nuances of how education experts think when conducting interactive story reading activities. To bridge this gap, we design an annotation framework, empowered by existing knowledge graph to capture experts’ annotations and thinking process, and leverage this framework to construct StorySparkQA dataset, which comprises 5, 868 expert-annotated QA pairs with real-world knowledge. We conduct automated and human expert evaluations across various QA pair generation settings to demonstrate that our StorySparkQA can effectively support models in generating QA pairs that target real-world knowledge beyond story content. StorySparkQA is available at https://huggingface.co/datasets/NEU-HAI/StorySparkQA.

2023

1. EMNLP 2023
   Findings

   Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture

   Bingsheng Yao, Ishan Jindal, Lucian Popa, Yannis Katsis, Sayan Ghosh, Lihong He, Yuxuan Lu, Shashank Srivastava, Yunyao Li, James Hendler, and Dakuo Wang

   In Findings of the Association for Computational Linguistics: EMNLP 2023, Dec 2023

   Abs arXiv PDF

   Real-world domain expertsD (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning techniques, such as Active Learning (AL), that aim to support human annotators mostly focus on the label while neglecting the natural language explanation of a data point. This work proposes a novel AL architecture to support experts’ real-world need for label and explanation annotations in low-resource scenarios. Our AL architecture leverages an explanation-generation model to produce explanations guided by human explanations, a prediction model that utilizes generated explanations toward prediction faithfully, and a novel data diversity-based AL sampling strategy that benefits from the explanation annotations. Automated and human evaluations demonstrate the effectiveness of incorporating explanations into AL sampling and the improved human annotation efficiency and trustworthiness with our AL architecture. Additional ablation studies illustrate the potential of our AL architecture for transfer learning, generalizability, and integration with large language models (LLMs). While LLMs exhibit exceptional explanation-generation capabilities for relatively simple tasks, their effectiveness in complex real-world tasks warrants further in-depth study.

2. TCBB 2023

   Improving Biomedical Question Answering by Data Augmentation and Model Weighting

   Yongping Du, Jingya Yan, Yuxuan Lu, Yiliang Zhao, and Xingnan Jin

   IEEE/ACM Transactions on Computational Biology and Bioinformatics, Dec 2023

2022

1. BCB 2022

   Contextual Embedding and Model Weighting by Fusing Domain Knowledge on Biomedical Question Answering

   Yuxuan Lu, Jingya Yan, Zhixuan Qi, Zhongzheng Ge, and Yongping Du

   In Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Dec 2022

   Abs arXiv

   Biomedical Question Answering aims to obtain an answer to the given question from the biomedical domain. Due to its high requirement of biomedical domain knowledge, it is difficult for the model to learn domain knowledge from limited training data. We propose a contextual embedding method that combines open-domain QA model AoA Reader and BioBERT model pre-trained on biomedical domain data. We adopt unsupervised pre-training on large biomedical corpus and supervised fine-tuning on biomedical question answering dataset. Additionally, we adopt an MLP-based model weighting layer to automatically exploit the advantages of two models to provide the correct answer. The public dataset biomrc constructed from PubMed corpus is used to evaluate our method. Experimental results show that our model outperforms state-of-the-art system by a large margin.

2021

1. BIBM 2021

   Dual Model Weighting Strategy and Data Augmentation in Biomedical Question Answering

   Yongping Du, Jingya Yan, Yiliang Zhao, Yuxuan Lu, and Xingnan Jin

   In 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Dec 2021

   PDF