14 citations · 22 across the 9 of their papers we have counts for
10 papers
Local Citation Recommendation Without Graph Signals
Benjamin Basseri, Pavlos Protopapas, Kemeng Zhang +3
Resolving correct citations can be a time-consuming task for research writers. We address local citation recommendation (LCR), where the goal is to retrieve appropriate references…
Do Lexical and Contextual Coreference Resolution Systems Degrade Differently under Mention Noise? An Empirical Study on Scientific Software Mentions
Atilla Kaan Alkan, Felix Grezes, Jennifer Lynn Bartlett +3
We present our participation in the SOMD 2026 shared task on cross-document software mention coreference resolution, where our systems ranked second across all three subtasks. We c…
AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics
Atilla Kaan Alkan, Felix Grezes, Sergi Blanco-Cuaresma +5
Scientific multi-label text classification suffers from extreme class imbalance, where specialized terminology exhibits severe power-law distributions that challenge standard class…
Predicting New Concept-Object Associations in Astronomy by Mining the Literature
Jinchu Li, Yuan-Sen Ting, Alberto Accomazzi +2
We construct a concept-object knowledge graph from the full astro-ph corpus through July 2025. Using an automated pipeline, we extract named astrophysical objects from OCR-processe…
AstroMLab 5: Structured Summaries and Concept Extraction for 400,000 Astrophysics Papers
Yuan-Sen Ting, Alberto Accomazzi, Tirthankar Ghosal +4
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:…
AstroMLab 4: Benchmark-Topping Performance in Astronomy Q&A with a 70B-Parameter Domain-Specialized Reasoning Model
Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal +7
General-purpose large language models (LLMs), despite their broad capabilities, often struggle with specialized domain knowledge. This gap hinders their deployment as reliable rese…
AstroMLab 3: Achieving GPT-4o Level Performance in Astronomy with a Specialized 8B-Parameter Large Language Model
Tijmen de Haan, Yuan-Sen Ting, Tirthankar Ghosal +6
AstroSage-Llama-3.1-8B is a domain-specialized natural-language AI assistant tailored for research in astronomy, astrophysics, cosmology, and astronomical instrumentation. Trained…
AstroMLab 2: AstroLLaMA-2-70B Model and Benchmarking Specialised LLMs for Astronomy
Rui Pan, Tuan Dung Nguyen, Hardik Arora +3
Continual pretraining of large language models on domain-specific data has been proposed to enhance performance on downstream tasks. In astronomy, the previous absence of astronomy…
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Kartheik G. Iyer, Mikaeel Yunus, Charles O'Neill +27
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We pr…
AstroMLab 1: Who Wins Astronomy Jeopardy!?
Yuan-Sen Ting, Tuan Dung Nguyen, Tirthankar Ghosal +8
We present a comprehensive evaluation of proprietary and open-weights large language models using the first astronomy-specific benchmarking dataset. This dataset comprises 4,425 mu…