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Found 5 papers, 1 papers with code
Disentangling Dense Embeddings with Sparse Autoencoders
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks.
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets
We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training.
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy.
Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy
This study investigates the application of Large Language Models (LLMs), specifically GPT-4, within Astronomy.
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