# SolarGPT-QA: A Domain-Adaptive Large Language Model for Educational Question Answering in Space Weather and Heliophysics

arXiv: https://arxiv.org/abs/2601.12131 · HTML: https://mlsanity.com/paper/2601.12131/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 69 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 30 of 30)

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- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
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- [CLLMate: A Multimodal Benchmark for Weather and Climate Events Forecasting](https://mlsanity.com/paper/2409.19058/) (2024)
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- [Contrastive Representation Learning for Predicting Solar Flares from Extremely Imbalanced Multivariate Time Series Data](https://mlsanity.com/paper/2410.00312/) (2024)
- [Advancing Hate Speech Detection with Transformers: Insights from the MetaHate](https://mlsanity.com/paper/2508.04913/) (2025)
- [Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse](https://mlsanity.com/paper/2411.13534/) (2024)
- [EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction](https://mlsanity.com/paper/2411.11249/) (2024)
- [Advancing Minority Stress Detection with Transformers: Insights from the Social Media Datasets](https://mlsanity.com/paper/2509.02908/) (2025)
- [Global Cross-Time Attention Fusion for Enhanced Solar Flare Prediction from Multivariate Time Series](https://mlsanity.com/paper/2511.12955/) (2025)

## Abstract (arXiv)

Solar activity, including solar flares, coronal mass ejections (CMEs), and geomagnetic storms can significantly impact satellites, aviation, power grids, data centers, and space missions. Extreme solar events can cause substantial economic damage with limited advance warning, underscoring the importance of early warning systems, accurate forecasting, and effective education in space science. Although large language models (LLMs) perform well on general tasks, they often lack domain specific knowledge and pedagogical capability to clearly explain complex space science concepts. We introduce SolarGPT-QA, a question answering system based on a domain adapted large language model built on the LLaMA-3 base model. The model is trained using scientific literature and large scale question and answer data generated with GPT-4 and refined using Grok-3 in a student friendly storytelling style. To evaluate response quality, we employ an LLM-as-judge evaluation framework, where a strong reference model assesses generated answers using structured criteria including scientific accuracy, clarity, completeness, and pedagogical effectiveness. Results show that SolarGPT-QA performs strongly relative to general purpose models in zero shot settings and achieves competitive performance compared to instruction tuned models for educational explanations in space weather and heliophysics. Ablation studies indicate that combining domain adaptive pretraining with fine tuning is important for balancing scientific accuracy and educational effectiveness.