arXiv subjects

Yi Bu

Publications and source records attributed to Yi Bu.

At least 37 records · Page 2Linked to original sources

arXiv2025

PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science

Papers, patents, and clinical trials are essential scientific resources in biomedicine, crucial for knowledge sharing and dissemination. However, these documents are often stored in disparate databases with varying management standards and data formats, making it challenging to form systematic and fine-grained connections among them. To address this issue, we construct PKG 2.0, a comprehensive knowledge graph dataset encompassing over 36 million papers, 1.3 million patents, and 0.48 million clinical trials in the biomedical field. PKG 2.0 integrates these dispersed resources through 482 million biomedical entity linkages, 19 million citation linkages, and 7 million project linkages. The construction of PKG 2.0 wove together fine-grained biomedical entity extraction, high-performance author name disambiguation, multi-source citation integration, and high-quality project data from the NIH Exporter. Data validation demonstrates that PKG 2.0 excels in key tasks such as author disambiguation and biomedical entity recognition. This dataset provides valuable resources for biomedical researchers, bibliometric scholars, and those engaged in literature mining.

cs.DL↗

arXiv2025

ValuesRAG: Enhancing Cultural Alignment Through Retrieval-Augmented Contextual Learning

Ensuring cultural values alignment in Large Language Models (LLMs) remains a critical challenge, as these models often embed Western-centric biases from their training data, leading to misrepresentations and fairness concerns in cross-cultural applications. Existing approaches such as role assignment and few-shot learning struggle to address these limitations effectively due to their reliance on pre-trained knowledge, limited scalability, and inability to capture nuanced cultural values. To address these issues, we propose ValuesRAG, a novel and effective framework that applies Retrieval-Augmented Generation (RAG) with In-Context Learning (ICL) to integrate cultural and demographic knowledge dynamically during text generation. Leveraging the World Values Survey (WVS) dataset, ValuesRAG first generates summaries of values for each individual. We subsequently curate several representative regional datasets to serve as test datasets and retrieve relevant summaries of values based on demographic features, followed by a reranking step to select the top-k relevant summaries. We evaluate ValuesRAG using 6 diverse regional datasets and show that it consistently outperforms baselines: including zero-shot, role-assignment, few-shot, and hybrid methods, both in main experiments and ablation settings. Notably, ValuesRAG achieves the best overall performance over prior methods, demonstrating its effectiveness in fostering culturally aligned and inclusive AI systems. Our findings underscore the potential of dynamic retrieval-based methods to bridge the gap between global LLM capabilities and localized cultural values.

cs.CL↗

arXiv2025

Dynamic disruption index across citation and cited references windows: Recommendations for thresholds in research evaluation

The temporal dimension of citation accumulation poses fundamental challenges for quantitative research evaluations, particularly in assessing disruptive and consolidating research through the disruption index (D). While prior studies emphasize minimum citation windows (mostly 3-5 years) for reliable citation impact measurements, the time-sensitive nature of D - which quantifies a paper' s capacity to eclipse prior knowledge - remains underexplored. This study addresses two critical gaps: (1) determining the temporal thresholds required for publications to meet citation/reference prerequisites, and (2) identifying "optimal" citation windows that balance early predictability and longitudinal validity. By analyzing millions of publications across four fields with varying citation dynamics, we employ some metrics to track D stabilization patterns. Key findings reveal that a 10-year window achieves >80% agreement with final D classifications, while shorter windows (3 years) exhibit instability. Publications with >=30 references stabilize 1-3 years faster, and extreme cases (top/bottom 5% D values) become identifiable within 5 years - enabling early detection of 60-80% of highly disruptive and consolidating works. The findings offer significant implications for scholarly evaluation and science policy, emphasizing the need for careful consideration of citation window length in research assessment (based on D).

cs.DL↗

arXiv2025

Political Hegemony, Imitation Isomorphism, and Project Familiarity: Instrumental Variables to Understand Funding Impact on Scholar Performance

This paper contributes a new idea for exploring research funding effects on scholar performance. By collecting details of 9,501 research grants received by principal investigators from universities in the U.S. social sciences from 2000 to 2019 and data on their publications and citations in the Microsoft Academic Graph and Web of Science bibliographic collections, we build a novel dataset of grants and article counts, citations, and journal CiteScore. Based on this dataset, we first introduce three instrumental variables (IVs) suitable for isolating endogeneity issues in the study of competing grant effects, namely scholars political hegemony in academia, imitation isomorphic behavior among scholars, and project familiarity. Then, this study explains the research funding effects by combining the three IVs with a two-stage least square (2SLS) model. Also, we provide validity and robustness tests of these three IVs and research funding effects. We find that our IVs serve the function of exogenizing and isolating endogeneity in capturing the research funding effect. Empirical findings show that receiving research funding increases a scholars research output and impact. While research funding doesn't significantly increase high CiteScore publications, it reduces submissions to low-prestige journals, reshaping journal selection strategies and raising the floor of academic performance.

cs.DL↗

arXiv2025

Transforming Role Classification in Scientific Teams Using LLMs and Advanced Predictive Analytics

Scientific team dynamics are critical in determining the nature and impact of research outputs. However, existing methods for classifying author roles based on self-reports and clustering lack comprehensive contextual analysis of contributions. Thus, we present a transformative approach to classifying author roles in scientific teams using advanced large language models (LLMs), which offers a more refined analysis compared to traditional clustering methods. Specifically, we seek to complement and enhance these traditional methods by utilizing open source and proprietary LLMs, such as GPT-4, Llama3 70B, Llama2 70B, and Mistral 7x8B, for role classification. Utilizing few-shot prompting, we categorize author roles and demonstrate that GPT-4 outperforms other models across multiple categories, surpassing traditional approaches such as XGBoost and BERT. Our methodology also includes building a predictive deep learning model using 10 features. By training this model on a dataset derived from the OpenAlex database, which provides detailed metadata on academic publications -- such as author-publication history, author affiliation, research topics, and citation counts -- we achieve an F1 score of 0.76, demonstrating robust classification of author roles.

cs.DL↗

arXiv2025

Position: Open and Closed Large Language Models in Healthcare

This position paper analyzes the evolving roles of open-source and closed-source large language models (LLMs) in healthcare, emphasizing their distinct contributions and the scientific community's response to their development. Due to their advanced reasoning capabilities, closed LLMs, such as GPT-4, have dominated high-performance applications, particularly in medical imaging and multimodal diagnostics. Conversely, open LLMs, like Meta's LLaMA, have gained popularity for their adaptability and cost-effectiveness, enabling researchers to fine-tune models for specific domains, such as mental health and patient communication.

cs.CY↗

arXiv2024

Who Gets Recommended? Investigating Gender, Race, and Country Disparities in Paper Recommendations from Large Language Models

This paper investigates the performance of several representative large models in the tasks of literature recommendation and explores potential biases in research exposure. The results indicate that not only LLMs' overall recommendation accuracy remains limited but also the models tend to recommend literature with greater citation counts, later publication date, and larger author teams. Yet, in scholar recommendation tasks, there is no evidence that LLMs disproportionately recommend male, white, or developed-country authors, contrasting with patterns of known human biases.

cs.IR↗

arXiv2024

Understanding Teams and Productivity in Information Retrieval Research (2000-2018): Academia, Industry, and Cross-Community Collaborations

Previous researches on the Information retrieval (IR) field have focused on summarizing progress and synthesizing knowledge and techniques from individual studies and data-driven experiments, the extent of contributions and collaborations between researchers from different communities (e.g., academia and industry) in advancing IR knowledge remains unclear. To address this gap, this study explores several characteristics of information retrieval research in four areas: productivity patterns and preferred venues, the relationship between citations and downloads, changes in research topics, and changes in patterns of scientific collaboration, by analyzing 53,471 papers published between 2000 and 2018 from the Association for Computing Machinery (ACM) Digital Library dataset. Through the analysis and interpretation on empirical datasets, we find that academic research, industry research, and collaborative research between academia and industry focused on different topics. Among the collaboration models, Academia-Industry Collaboration is more oriented towards large teamwork. Collaborative networks between researchers in academia and industry suggest that the field of information retrieval has become richer over time in terms of themes, foci, and sub-themes, becoming a more diverse field of study.

cs.DL↗

arXiv2024

The Unique Taste of LLMs for Papers: Potential issues in Using LLMs for Digital Library Document Recommendation Tasks

This paper investigates the performance of several representative large models in the field of literature recommendation and explores potential biases. The results indicate that while some large models' recommendations can be somewhat satisfactory after simple manual screening, overall, the accuracy of these models in specific literature recommendation tasks is generally moderate. Additionally, the models tend to recommend literature that is timely, collaborative, and expands or deepens the field. In scholar recommendation tasks. There is no evidence to suggest that LLMs exacerbate inequalities related to gender, race, or the level of development of countries.

cs.DL↗

arXiv2024

Effects of Same-Race Mentorship Preferences on Academic Performance and Survival

Same-race mentorship preference refers to mentors or mentees forming connections significantly influenced by a shared race. Although racial diversity in science has been well-studied and linked to favorable outcomes, the extent and effects of same-race mentorship preferences remain largely underexplored. Here, we analyze 465,355 mentor-mentee pairs from more than 60 research areas over the last 70 years to investigate the effect of same-race mentorship preferences on mentees' academic performance and survival. We use causal inference and statistical matching to measure same-race mentorship preferences while accounting for racial demographic variations across institutions, time periods, and research fields. Our findings reveal a pervasive same-race mentorship propensity across races, fields, and universities of varying research intensity. We observe an increase in same-race mentorship propensity over the years, further reinforced inter-generationally within a mentorship lineage. This propensity is more pronounced for minorities (Asians, Blacks, and Hispanics). Our results reveal that mentees under the supervision of mentors with high same-race propensity experience significantly lower productivity, impact, and collaboration reach during and after training, ultimately leading to a 27.6% reduced likelihood of remaining in academia. In contrast, a mentorship approach devoid of racial propensity appears to offer the best prospects for academic performance and persistence. These findings underscore the importance of mentorship diversity for academic success and shed light on factors contributing to minority underrepresentation in science.

cs.SI↗

arXiv2024

Towards the relationship between AIGC in manuscript writing and author profiles: evidence from preprints in LLMs

AIGC tools such as ChatGPT have profoundly changed scientific research, leading to widespread attention on its use on academic writing. Leveraging preprints from large language models, this study examined the use of AIGC in manuscript writing and its correlation with author profiles. We found that: (1) since the release of ChatGPT, the likelihood of abstracts being AI-generated has gradually increased; (2) scientists from English-speaking countries are less likely to use AIGC tools for writing assistance, while those from countries with linguistic differences from English are more likely to use these tools; (3) there is weak correlation between a paper's AI-generated probability and authors' academic performance; and (4) authors who have previously published papers with high AI-generated probabilities are more likely to continue using AIGC tools. We believe that this paper provides insightful results for relevant policies and norms and in enhancing the understanding of the relationship between humans and AI.

cs.DL↗

arXiv2024

The prominent and heterogeneous gender disparities in scientific novelty: evidence from biomedical doctoral theses

Scientific novelty is the essential driving force for research breakthroughs and innovation. However, little is known about how early-career scientists pursue novel research paths, and the gender disparities in this process. To address this research gap, this study investigates a comprehensive dataset of 279,424 doctoral theses in biomedical sciences authored by US Ph.D. graduates. Spanning from 1980 to 2016, the data originates from the ProQuest Dissertations & Theses Database. This study aims to shed light on Ph.D. students' pursuit of scientific novelty in their doctoral theses and assess gender-related differences in this process. Using a combinatorial approach and a pre-trained Bio-BERT model, we quantify the scientific novelty of doctoral theses based on bio-entities. Applying fractional logistic and quantile regression models, this study reveals a decreasing trend in scientific novelty over time and heterogeneous gender disparities in doctoral theses. Specifically, female students consistently exhibited lower scientific novelty levels than their male peers. When supervised by female advisors, students' theses are found to be less novel than those under male advisors. The significant interaction effect of female students and female advisors suggests that female advisors may amplify the gender disparity in scientific novelty. Moreover, heterogeneous gender disparities in scientific novelty are identified, with non-top-tier universities displaying more pronounced disparities, while the differences at higher percentile ranges were comparatively more minor. These findings indicate a potential underrepresentation of female scientists pursuing novel research during the early stages of their careers. Notably, the outcomes of this study hold significant policy implications for advancing the careers of female scientists.

physics.soc-ph↗

arXiv2023

What makes a successful rebuttal in computer science conferences? : A perspective on social interaction

With an exponential increase in submissions to top-tier Computer Science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be modeled as social interactions between authors and reviewers. A successful rebuttal often results in an increased review score after the rebuttal stage. In this paper, we conduct an empirical study to determine the factors contributing to a successful rebuttal using over 3,000 papers and 13,000 reviews from ICLR2022, one of the most prestigious computer science conferences. First, we observe a significant difference in review scores before and after the rebuttal stage, which is crucial for paper acceptance. Furthermore, we investigate factors from the reviewer's perspective using signed social network analysis. A notable finding is the increase in balanced network structure after the rebuttal stage. Subsequently, we evaluate several quantifiable author rebuttal strategies and their effects on review scores. These strategies can help authors in improving their review scores. Finally, we used machine learning models to predict rebuttal success and validated the impact of potential factors analyzed in this paper. Our experiments demonstrate that the utilization of all features proposed in this study can aid in predicting the success of the rebuttal. In summary, this work presents a study on the impact factors of successful rebuttals from both reviewers' and authors' perspectives and lays the foundation for analyzing rebuttals with social network analysis.

cs.SI↗

arXiv2023

Has China caught up to the US in AI research? An exploration of mimetic isomorphism as a model for late industrializers

Artificial Intelligence (AI), a cornerstone of 21st-century technology, has seen remarkable growth in China. In this paper, we examine China's AI development process, demonstrating that it is characterized by rapid learning and differentiation, surpassing the export-oriented growth propelled by Foreign Direct Investment seen in earlier Asian industrializers. Our data indicates that China currently leads the USA in the volume of AI-related research papers. However, when we delve into the quality of these papers based on specific metrics, the USA retains a slight edge. Nevertheless, the pace and scale of China's AI development remain noteworthy. We attribute China's accelerated AI progress to several factors, including global trends favoring open access to algorithms and research papers, contributions from China's broad diaspora and returnees, and relatively lax data protection policies. In the vein of our research, we have developed a novel measure for gauging China's imitation of US research. Our analysis shows that by 2018, the time lag between China and the USA in addressing AI research topics had evaporated. This finding suggests that China has effectively bridged a significant knowledge gap and could potentially be setting out on an independent research trajectory. While this study compares China and the USA exclusively, it's important to note that research collaborations between these two nations have resulted in more highly cited work than those produced by either country independently. This underscores the power of international cooperation in driving scientific progress in AI.

cs.AI↗

arXiv2023

The Impact of Heterogeneous Shared Leadership in Scientific Teams

Leadership is evolving dynamically from an individual endeavor to shared efforts. This paper aims to advance our understanding of shared leadership in scientific teams. We define three kinds of leaders, junior (10-15), mid (15-20), and senior (20+) based on career age. By considering the combinations of any two leaders, we distinguish shared leadership as heterogeneous when leaders are in different age cohorts and homogeneous when leaders are in the same age cohort. Drawing on 1,845,351 CS, 254,039 Sociology, and 193,338 Business teams with two leaders in the OpenAlex dataset, we identify that heterogeneous shared leadership brings higher citation impact for teams than homogeneous shared leadership. Specifically, when junior leaders are paired with senior leaders, it significantly increases team citation ranking by 1-2%, in comparison with two leaders of similar age. We explore the patterns between homogeneous leaders and heterogeneous leaders from team scale, expertise composition, and knowledge recency perspectives. Compared with homogeneous leaders, heterogeneous leaders are more adaptive in large teams, have more diverse expertise, and trace both the newest and oldest references.

physics.soc-ph↗

arXiv2022

Exploring the Distribution Regularities of User Attention and Sentiment toward Product Aspects in Online Reviews

[Purpose] To better understand the online reviews and help potential consumers, businessmen, and product manufacturers effectively obtain users' evaluation on product aspects, this paper explores the distribution regularities of user attention and sentiment toward product aspects from the temporal perspective of online reviews. [Design/methodology/approach] Temporal characteristics of online reviews (purchase time, review time, and time intervals between purchase time and review time), similar attributes clustering, and attribute-level sentiment computing technologies are employed based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of user attention and sentiment toward product aspects in this article. [Findings] The empirical results show that a power-law distribution can fit user attention to product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of user sentiment of product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product. [Research limitations] The paper can't acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms. [Originality/value] This work reveals the distribution regularities of user attention and sentiment toward product aspects, which is of great significance in assisting decision-making, optimizing review presentation, and improving the shopping experience.

cs.CL↗

arXiv2022

Team formation and team performance: The balance between team freshness and repeat collaboration

Incorporating fresh members in teams is considered a pathway to team creativity. However, whether freshness improves team performance or not remains unclear, as well as the optimal involvement of fresh members for team performance. This study uses a group of authors on the byline of a publication as a proxy for a scientific team. We extend an indicator, i.e., team freshness, to measure the extent to which a scientific team incorporates new members, by calculating the fraction of new collaboration relations established within the team. Based on more than 43 million scientific publications covering more than a half-century of research from Microsoft Academic Graph, this study provides a holistic picture of the current development of team freshness by outlining the temporal evolution of freshness, and its disciplinary distribution. Subsequently, using a multivariable regression approach, we examine the association between team freshness and papers'short-term and long-term citations.The major findings are as follows: (1)team freshness in scientific teams has been increasing in the past half-century; (2)there exists an inverted-U-shaped association between team freshness and papers' citations in all the disciplines and in different periods;(3)the inverted-U-shaped relationship between team freshness and papers' citations is only found in small teams, while, in large teams, team freshness is significantly positively related to papers' citations.

cs.DL↗

arXiv2022

Team Power Dynamics and Team Impact: New Perspectives on Scientific Collaboration using Career Age as a Proxy for Team Power

Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all co-authors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of co-authors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.

physics.soc-ph↗