A Hazard Analysis Framework for Code Synthesis Large Language Models
OpenAI has developed a hazard analysis framework designed to uncover safety risks and hazards associated with the deployment of large language models (LLMs) capable of advanced code synthesis, such as Codex. This framework is necessary because while code generation models offer significant benefits, they also introduce potential for misuse, alignment problems, and the possibility of accelerating progress in technical fields that could have destabilizing effects.
Multidimensional Hazard Analysis
The hazard analysis framework evaluates the safety risks of code synthesis models across four primary dimensions:
- Technical Risks: Assessing the inherent limitations and potential for the model to generate insecure or incorrect code.
- Social Risks: Analyzing how the deployment of these models affects social structures and interactions.
- Political Risks: Evaluating the potential for models to be used in ways that impact political stability or processes.
- Economic Risks: Examining the potential for economic disruption caused by the automation of code synthesis.
Evaluation Framework for Code Generation Capability
To inform the hazard analysis, OpenAI utilizes a novel evaluation framework that measures the capacity of advanced code generation techniques. This evaluation focuses on two key metrics:
- Specification Prompt Complexity: The framework determines how the model handles the complexity and expressivity of the prompts used to specify the desired code.
- Human-Relative Capability: The framework assesses the model's ability to understand and execute these specifications relative to human ability.
By quantifying the model's capabilities against these benchmarks, OpenAI can better identify the specific thresholds at which a model's capacity to generate code might transition from a helpful tool to a potential hazard.