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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 6·5 min read
What We Observe as LLM Behavior Can Be a Side-effect of Inference Backend
TL;DR
Different inference backends significantly affect the performance of large language models (LLMs), with backend choice accounting for about 39% of variability in benchmark scores. This study highlights the importance of disclosing backend details and generation configurations for accurate model evaluation.
✦ Why It Matters
Ensure to disclose your model's inference backend and configuration for accurate benchmarking.
Key Takeaways
- Disclose the inference backend and its version when reporting model performance.
- Use deterministic decoding for consistent cross-backend comparisons.
- Understand that backend choice can significantly impact benchmark scores.
- Consider backend variability when interpreting LLM performance metrics.
How It Works
The study employs a fully-crossed experimental design to systematically evaluate how different inference frameworks affect LLM outputs. By varying the backend and analyzing the resulting benchmark scores, the researchers quantify the impact of backend choice on model performance.