Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·May 26·5 min read
Benchmarking Patent Embeddings: A Multi-Task Evaluation of 22 Models Across Retrieval, Classification, and Clustering
TL;DR
Practitioners face challenges in using patent embeddings effectively across different tasks and datasets. This study evaluated 22 pre-trained models on information retrieval, classification, and clustering tasks using a large dataset of WIPO patents.
✦ Why It Matters
Engineers can optimize patent embedding models by selecting task-specific fine-tuning strategies to improve performance.
Key Takeaways
- Different tasks require tailored fine-tuning strategies.
- Cross-sectional alignment improves retrieval performance significantly.
- Single-landscape fine-tuning can degrade cross-domain retrieval.
- A 55-65% performance gap exists between in-domain and out-of-domain tasks.
How It Works
The study evaluates various fine-tuning recipes for patent embeddings, determining that task-specific strategies yield better results. For retrieval, cross-sectional alignment enhances performance, while a combined signal approach is more effective for classification and clustering.