AstraQ-VL: Parameter-Efficient Astronomy Vision-Language Modeling with Connector Alignment and LoRA Tuning
AstraQ-VL: Parameter-Efficient Astronomy Vision-Language Modeling with Connector Alignment and LoRA Tuning
AstraQ-VL is a compact LLaVA-style astronomy vision-language model that combines a frozen CLIP ViT-L/14 vision encoder, a two-layer MLP connector, and Qwen2.5-1.5B-Instruct. Stage 1 trains only the connector, while Stage 2 warm-starts the connector and adds LoRA adapters to the language model.The paper compares the two stages on an untouched image-disjoint internal test set and evaluates their external behavior through zero-shot prompted closed-set classification on AstroVLBench Tasks 1–2 and single-reference solar-image captioning on DeepSDO. Stage 2 improves automatic reference alignment on the internal test set and the selected AstroVLBench aggregate, but its external behavior is task-dependent: it regresses on FIRST and does not improve the DeepSDO overlap measures. All reported outcomes are automatic measurements and do not establish scientific correctness or factual reliability.
multimodal learning, Qwen, LLaVA, automatic evaluation, AstraQ-VL, CLIP, vision-language models, Astrophysics, LoRA, astronomy, Machine Learning, Artificial Intelligence, Computer Science, parameter-efficient fine-tuning
multimodal learning, Qwen, LLaVA, automatic evaluation, AstraQ-VL, CLIP, vision-language models, Astrophysics, LoRA, astronomy, Machine Learning, Artificial Intelligence, Computer Science, parameter-efficient fine-tuning
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