Sejal Gupta | Searcharxiv

arXiv2026

RetraLytix: An Integrated Analytics Dashboard for Mapping Global Trends in Scientific Retractions

Retraction is a correction to scientific literature when there is a major flaw, fraud or misuse of ethical practices in the published work. With the increasing growth of research output, number of retracted studies has also increased, which raises concerns about the issue of research ethics and transparency. Moreover, retraction data coming from several platforms or databases limits its scope in tracking the time-to-time retraction trends. To address this, we propose a web-based integrated platform, called RetraLytix, for easy analysis of distributed retraction data. It automatically integrates retraction data from major databases like Crossref, Retraction Watch and Open Alex and visualizes data in a user interactive centralized platform. It offers a real-time dashboard, comparative analysis, and benchmarking of entities such as countries, institutions, authors, journals and main research areas. RetraLytix helps users to detect trends, retraction patterns, and assess research environment to make data-driven decisions. The system has a potential to become a research integrity tracking and governance tool for researchers, administrators and policymakers.

cs.DL↗

arXiv2024

MDCR: A Dataset for Multi-Document Conditional Reasoning

The same real-life questions posed to different individuals may lead to different answers based on their unique situations. For instance, whether a student is eligible for a scholarship depends on eligibility conditions, such as major or degree required. ConditionalQA was proposed to evaluate models' capability of reading a document and answering eligibility questions, considering unmentioned conditions. However, it is limited to questions on single documents, neglecting harder cases that may require cross-document reasoning and optimization, for example, "What is the maximum number of scholarships attainable?" Such questions over multiple documents are not only more challenging due to more context having to understand, but also because the model has to (1) explore all possible combinations of unmentioned conditions and (2) understand the relationship between conditions across documents, to reason about the optimal outcome. To evaluate models' capability of answering such questions, we propose a new dataset MDCR, which can reflect real-world challenges and serve as a new test bed for complex conditional reasoning that requires optimization. We evaluate this dataset using the most recent LLMs and demonstrate their limitations in solving this task. We believe this dataset will facilitate future research in answering optimization questions with unknown conditions.

cs.CL↗