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Yue Guo

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Computation and Language Machine Learning hep-ex math.DS math.GR math.NA Numerical Analysis physics.ins-det Robotics

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preprint2026arXiv

ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?

Plain Language Summaries (PLS) aim to make research accessible to lay readers, but they are typically written in a one-size-fits-all style that ignores differences in readers' information needs and comprehension. In health contexts, this limitation is particularly important because misunderstanding scientific information can affect real-world decisions. Large language models (LLMs) offer new opportunities for personalizing PLS, but it remains unclear whether personalization helps, which strategies are most effective, and how to balance personalization with safety. We introduce ReLay, a dataset of 300 participant--PLS pairs from 50 lay participants in both static (expert-written) and interactive (LLM-personalized) settings. ReLay includes user characteristics, health information needs, information-seeking behavior, comprehension outcomes, interaction logs, and quality ratings. We use ReLay to evaluate five LLMs across two personalization methods. Personalization improves comprehension and perceived quality, but it also raises the risk of reinforcing user biases and introducing hallucinations, revealing a trade-off between personalization and safety. These findings highlight the need for personalization methods that are both effective and trustworthy for diverse lay audiences.

preprint2022arXiv

Automated Lay Language Summarization of Biomedical Scientific Reviews

Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes. However, medical jargon and the complex structure of professional language in this domain make health information especially hard to interpret. Thus, there is an urgent unmet need for automated methods to enhance the accessibility of the biomedical literature to the general population. This problem can be framed as a type of translation problem between the language of healthcare professionals, and that of the general public. In this paper, we introduce the novel task of automated generation of lay language summaries of biomedical scientific reviews, and construct a dataset to support the development and evaluation of automated methods through which to enhance the accessibility of the biomedical literature. We conduct analyses of the various challenges in solving this task, including not only summarization of the key points but also explanation of background knowledge and simplification of professional language. We experiment with state-of-the-art summarization models as well as several data augmentation techniques, and evaluate their performance using both automated metrics and human assessment. Results indicate that automatically generated summaries produced using contemporary neural architectures can achieve promising quality and readability as compared with reference summaries developed for the lay public by experts (best ROUGE-L of 50.24 and Flesch-Kincaid readability score of 13.30). We also discuss the limitations of the current attempt, providing insights and directions for future work.

preprint2022arXiv

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

We study the learning of numerical algorithms for scientific computing, which combines mathematically driven, handcrafted design of general algorithm structure with a data-driven adaptation to specific classes of tasks. This represents a departure from the classical approaches in numerical analysis, which typically do not feature such learning-based adaptations. As a case study, we develop a machine learning approach that automatically learns effective solvers for initial value problems in the form of ordinary differential equations (ODEs), based on the Runge-Kutta (RK) integrator architecture. We show that we can learn high-order integrators for targeted families of differential equations without the need for computing integrator coefficients by hand. Moreover, we demonstrate that in certain cases we can obtain superior performance to classical RK methods. This can be attributed to certain properties of the ODE families being identified and exploited by the approach. Overall, this work demonstrates an effective learning-based approach to the design of algorithms for the numerical solution of differential equations. This can be readily extended to other numerical tasks.

preprint2015arXiv

Arbitrary Functions in Group Theory

Two measures of how near an arbitrary function between groups is to being a homomorphism are considered. These have properties similar to conjugates and commutators. The authors show that there is a rich theory based on these structures, and that this theory can be used to unify disparate approaches such as group cohomology and the transfer and to prove theorems. The proof of the Schur-Zassenhaus theorem is recast in this context. We also present yet another proof of Cauchy's theorem and a very quick approach to Sylow's theorem.

preprint2015arXiv

Study of cluster reconstruction and track fitting algorithms for CGEM-IT at BESIII

Considering the aging effects of existing Inner Drift Chamber (IDC) of BES\uppercase\expandafter{\romannumeral3}, a GEM based inner tracker is proposed to be designed and constructed as an upgrade candidate for IDC. This paper introduces a full simulation package of CGEM-IT with a simplified digitization model, describes the development of the softwares for cluster reconstruction and track fitting algorithm based on Kalman filter method for CGEM-IT. Preliminary results from the reconstruction algorithms are obtained using a Monte Carlo sample of single muon events in CGEM-IT.

preprint2014arXiv

Research on the mobile robots intelligent path planning based on ant colony algorithm application in manufacturing logistics

With the development of robotics and artificial intelligence field unceasingly thorough, path planning as an important field of robot calculation has been widespread concern. This paper analyzes the current development of robot and path planning algorithm and focuses on the advantages and disadvantages of the traditional intelligent path planning as well as the path planning. The problem of mobile robot path planning is studied by using ant colony algorithm, and it also provides some solving methods.