The Workshop for Artificial Intelligence for Scientific Publications (WASP), formerly Workshop on Information Extraction from Scientific Publications (WIESP), started in 2022 to provide a platform for researchers to discuss research on information extraction, mining, generation, and knowledge discovery from scientific publications using Natural Language Processing and Machine Learning techniques. The third WASP workshop was held at the 14th International Joint Conference on Natural Language Processing and 4th Asia-Pacific Chapter of the Association for Computational Linguistics in Mumbai, India on December 23rd, 2025, as a hybrid event. The WASP workshop saw great interest, with 29 submissions, of which 16 were accepted. The program consisted of the contributed research talks, 2 keynote talks, a panel discussion, and one shared task, Telescope Reference and Astronomy Categorization Shared task (TRACS).

To evaluate the scientific influence of observational facilities, astronomers examine the body of publications that have utilized data from those facilities. This depends on curated bibliographies that annotate and connect data products to the corresponding literature, enabling bibliometric analyses to quantify data impact. Compiling such bibliographies is a demanding process that requires expert curators to scan the literature for relevant names, acronyms, and identifiers, and then to determine whether and how specific observations contributed to each publication. These bibliographies have value beyond impact assessment: for research scientists, explicit links between data and literature form an essential pathway for discovering and accessing data. Accordingly, by building on the work of librarians and archivists, telescope bibliographies can be repurposed to directly support scientific inquiry. In this context, we present the Telescope Reference and Astronomy Categorization Shared task (TRACS) and its accompanying dataset, which comprises more than 89,000 publicly available English-language texts drawn from space telescope bibliographies. These texts are labeled according to a new, compact taxonomy developed in consultation with experienced bibliographers.

We present a dataset of 408,590 astrophysics papers from arXiv (astro-ph), spanning 1992 through July 2025. Each paper has been processed through a multi-stage pipeline to produce: (1) structured summaries organized into six semantic sections (Background, Motivation, Methodology, Results, Interpretation, Implication), and (2) concept extraction yielding 9,999 unique concepts with detailed descriptions. The dataset contains 3.8 million paper-concept associations and includes semantic embeddings for all concepts. Comparison with traditional ADS keywords reveals that the concepts provide denser coverage and more uniform distribution, while analysis of embedding space structure demonstrates that concepts are semantically dispersed within papers—enabling discovery through multiple diverse entry points. Concept vocabulary and embeddings are publicly released at https://github.com/tingyuansen/astro-ph_knowledge_graph.

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a comprehensive suite of LLMs tailored for the closely-related domains of Earth science, biology, physics, heliophysics, planetary sciences and astrophysics, and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints. We also created three new scientific benchmark datasets, Climate-Change NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. We show that our models outperform both general-purpose (RoBERTa) and domain- specific (SciBERT) encoders on these new tasks as well as existing tasks in the domains of interest. Furthermore, we demonstrate the use of these models in two industrial settings- as a retrieval model for large-scale vector search applications and in automatic content tagging systems.