Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Lost in Serialization: Invariance and Generalization of LLM Graph Reasoners
TL;DR
Graph reasoners using Large Language Models (LLMs) struggle with invariance to graph representation changes, leading to inconsistent outputs. The authors analyzed the impact of fine-tuning on encoding sensitivity and generalization, proposing a decomposition of graph serializations into node labeling, edge encoding, and syntax.
✦ Why It Matters
Engineers can improve LLM-based graph reasoners by understanding the trade-offs of fine-tuning and model size on robustness.
Key Takeaways
- Assess LLM sensitivity to graph representation changes.
- Evaluate the impact of fine-tuning on model robustness.
- Utilize the proposed benchmarking suite for testing.
- Explore spectral tasks to enhance generalization assessment.
How It Works
The study decomposes graph serializations into node labeling, edge encoding, and syntax, allowing for targeted analysis of how each component affects model performance. By systematically varying these components, the authors can measure the robustness of LLMs in response to different graph representations.