Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·10 min read
Explaining Black-Box Language Models: Learning to Optimize Linguistically-Structured Word Subsets
TL;DR
Deep language models (DLMs) often operate as black boxes, making it difficult to understand their decision-making processes. A new method was developed to explain DLM predictions by selecting informative subsets of input words, using an optimization approach that is efficient and interpretable.
✦ Why It Matters
Engineers can implement this method to enhance the interpretability of their DLM applications in sensitive domains.
Key Takeaways
- Utilize the proposed method for better model interpretability.
- Select informative word subsets to explain predictions effectively.
- Integrate graph-structured knowledge for coherent explanations.
- Evaluate performance across various DLM architectures and datasets.
How It Works
The method formulates the explanation process as an amortized optimization problem, allowing for efficient one-shot inference. It uses REINFORCE-style policy gradients to select discrete words without requiring gradient information from the black-box model.
By incorporating graph-structured knowledge, the selected word subsets are linguistically coherent, enhancing the interpretability of the explanations.