EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 23·5 min read
Cross-entropy comparison of LLM responses reveals Kimi's similarity to Claude
TL;DR
A heat map analysis of large language models (LLMs) reveals that Kimi and Claude exhibit significant similarities in their writing styles. This comparison is based solely on the cross-entropy of their responses.
✦ Why It Matters
Evaluate your choice of LLMs based on their writing style similarities to optimize your projects.
Key Takeaways
- Use cross-entropy to compare LLM outputs effectively.
- Analyze character trigrams and word unigrams for deeper insights.
- Visualize model similarities with heat maps for clarity.
- Leverage findings to inform your model development strategies.
How It Works
The analysis employs cross-entropy to quantify the similarity between the outputs of Kimi and Claude. By counting character trigrams and word unigrams, the study creates a statistical framework to compare the linguistic patterns of both models.
The resulting heat map visually represents these similarities, making it easier to identify shared characteristics in their responses.