2024

1. 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.

2. In Submission

   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

   In Submission, 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.

3. 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  others

   2024

   arXiv PDF

2023

1. arXiv

   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

   arXiv preprint arXiv:2311.09782, 2023

   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. 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.

3. arXiv

   FairytaleCQA: Integrating a Commonsense Knowledge Graph into Children’s Storybook Narratives

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

   arXiv preprint arXiv:2311.09756, 2023

   Abs arXiv PDF

   AI models (including LLM) often rely on narrative question-answering (QA) datasets to provide customized QA functionalities to support downstream children education applications; however, existing datasets only include QA pairs that are grounded within the given storybook content, but children can learn more when teachers refer the storybook content to real-world knowledge (e.g., commonsense knowledge). We introduce the FairytaleCQA dataset, which is annotated by children education experts, to supplement 278 storybook narratives with educationally appropriate commonsense knowledge. The dataset has 5,868 QA pairs that not only originate from the storybook narrative but also contain the commonsense knowledge grounded by an external knowledge graph (i.e., ConceptNet). A follow-up experiment shows that a smaller model (T5-large) fine-tuned with FairytaleCQA reliably outperforms much larger prompt-engineered LLM (e.g., GPT-4) in this new QA-pair generation task (QAG). This result suggests that: 1) our dataset brings novel challenges to existing LLMs, and 2) human experts’ data annotation are still critical as they have much nuanced knowledge that LLMs do not know in the children educational domain.

4. 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.

5. 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

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