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Paper Bnd90G3Sfhpbystd Explanation Page
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Figures Explained
Figure 1: Learning curve of AstroLLaMA during its fine-tuning on the arXiv astrophysics dataset. The Fig.tracks the evolution of perplexity, a measure of the modelu2019s next-token prediction performance. The light blue curve shows the training perplexity at each AdamW update step, while the dark black curve pro- vides a smoothed average taken over 10-step intervals.
Figure 1: Learning curve of AstroLLaMA during its fine-tuning on the arXiv astrophysics dataset. The Fig.tracks the evolution of perplexity, a measure of the modelu2019s next-token prediction performance. The light blue curve shows the training perplexity at each AdamW update step, while the dark black curve pro- vides a smoothed average taken over 10-step intervals.
Fig. 1 depicts the performance of AstroLLaMA during its fine-tuning phase. Here, we present per- plexity, a commonly used metric for evaluating causal language models. Perplexity is defined as
Fig. 1 depicts the performance of AstroLLaMA during its fine-tuning phase. Here, we present per- plexity, a commonly used metric for evaluating causal language models. Perplexity is defined as
Figure 2: Completion of an abstract from the arXiv database (ID: 2306.15719) using three different models: GPT-4, LLaMA-2, and AstroLLaMA. Each model is prompted with the same short text snippet, highlighted in their respective boxes. GPT-4 tends to produce more generic statements, lacking domain-specific nuance. AstroLLaMA demonstrates the most robust completion, offering more relevant concepts and deeper insights specific to the field of astronomy, thus significantly outperforming LLaMA-2 and GPT-4.
Figure 2: Completion of an abstract from the arXiv database (ID: 2306.15719) using three different models: GPT-4, LLaMA-2, and AstroLLaMA. Each model is prompted with the same short text snippet, highlighted in their respective boxes. GPT-4 tends to produce more generic statements, lacking domain-specific nuance. AstroLLaMA demonstrates the most robust completion, offering more relevant concepts and deeper insights specific to the field of astronomy, thus significantly outperforming LLaMA-2 and GPT-4.
Figure 3: Top: Distribution of pairwise cosine similari- ties among 10,000 randomly selected abstracts from our corpus, divided into 10 equal bins based on similarity levels from GPT-3. Bottom: Two representative exam- ples illustrating divergent cosine similarity values when comparing AstroLLaMA and GPT-3 embeddings.
Figure 3: Top: Distribution of pairwise cosine similari- ties among 10,000 randomly selected abstracts from our corpus, divided into 10 equal bins based on similarity levels from GPT-3. Bottom: Two representative exam- ples illustrating divergent cosine similarity values when comparing AstroLLaMA and GPT-3 embeddings.