Automatic Model Card Generation Using an LLM

arXiv:2608.24807 · cs.SE, cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.SE, cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/google/ml-metadata

Terminology

• Semantic Similarity — 31× in this paper · explained in 2 episodes
• Hallucination — 19× in this paper · explained in 3 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Consistency — 15× in this paper · explained in 3 episodes
• Cosine Similarity — 14× in this paper · explained in 3 episodes
• Accountability — 6× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Alignment — 4× in this paper · explained in 1 episode
• Grounding — 4× in this paper · explained in 3 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Interactive Model Card — 2× in this paper
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Frustration — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Git Large File Storage — 1× in this paper
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Nadia — 1× in this paper · explained in 2 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Efficient Prompting Methods for Large Language Models: A Survey
• SPECTER: Document-level Representation Learning using Citation-informed Transformers
• Understanding the Effects of Iterative Prompting on Truthfulness
• Software Engineering and Foundation Models: Insights from Industry Blogs Using a Jury of Foundation Models
• What's documented in AI? Systematic Analysis of 32K AI Model Cards
• Towards evaluating and eliciting high-quality documentation for intelligent systems
• A Methodology for Creating AI FactSheets
• Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models
• Progressive-Hint Prompting Improves Reasoning in Large Language Models

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