EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Jul 22·5 min read
Can a MUD evaluate LLMs? A $99 proof of concept
TL;DR
CrucibleBench introduces a novel AI evaluation method using a multi-user dungeon (MUD) to measure model behavior in social contexts, revealing limitations of static benchmarks.
✦ Why It Matters
Consider using MUDs for evaluating AI models in social contexts to gain deeper insights into their behavior.
Key Takeaways
- Utilize MUDs for innovative AI model evaluations.
- Focus on dynamic interactions rather than static benchmarks.
- Implement per-subject audits for more reliable rankings.
- Explore the full release of transcripts and scoring code.
How It Works
CrucibleBench uses a MUD to create a controlled environment where LLMs interact with NPCs, allowing for measurable behaviors based on trust and social feedback. The MUD's limited command space and persistent state enable clear detection of hallucinated actions and exploration patterns, providing insights into model performance.