Assessing Risks of Language Models in Real-World Scenarios
Recent advancements in language models (LMs) and their usage in various tools have paved the way for semi-autonomous agents that operate in real-world scenarios. While these agents bring about exciting possibilities and enhanced capabilities, they also pose significant risks if not properly managed. Failures to follow instructions could lead to serious consequences such as financial losses, property damage, and even life-threatening situations. It is crucial, therefore, to thoroughly assess and identify any potential risks associated with these language models before deploying them.
The process of identifying these risks is complex due to their open-ended nature and the extensive engineering effort required for testing. Traditionally, human experts set up sandboxes, use specific tools, and scrutinize agent executions in a labor-intensive process that limits scalability. To overcome these challenges, researchers have developed a new framework called ToolEmu that leverages advances in language models and emulation techniques to examine LM agents across various tools and scenarios.
At the core of ToolEmu is the use of a language model to emulate tools and their execution sandboxes. Unlike traditional simulated environments, ToolEmu utilizes recent LM advancements to emulate tool execution using specifications and inputs. This allows for rapid prototyping of LM agents and accommodates high-stakes tools that may not have existing APIs or sandbox implementations. For example, the emulator has already uncovered failures in traffic control scenarios, highlighting the potential risks involved.
To enhance risk assessment, an adversarial emulator is introduced, enabling the identification of potential failure modes in LM agents through red-teaming. This approach has proven to be effective, with a significant number of failures identified by human evaluators being deemed realistic and genuinely risky.
To support scalable risk assessments, an LM-based safety evaluator quantifies potential failures and assesses their associated risk severities. This automatic evaluator has shown promising results, identifying a large proportion of failures detected by human evaluators. Additionally, an automatic helpfulness evaluator quantifies the trade-off between safety and helpfulness, demonstrating comparable agreement rates with human annotations.
These emulators and evaluators contribute to the development of a benchmark for quantitative LM agent assessments across a wide range of tools and scenarios. With a focus on a threat model involving ambiguous user instructions, the benchmark includes 144 test cases covering different risk types and spanning multiple tools. Evaluation results have shown that API-based LMs like GPT-4 and Claude-2 have achieved top scores in safety and helpfulness, and further refinement has led to improved performance. However, even the safest LM agents still exhibit failures in a significant number of test cases, emphasizing the ongoing need for efforts to enhance LM agent safety.
While this research holds promise for identifying and minimizing risks associated with language models, there is still much work to be done to ensure the safe and effective deployment of these agents in real-world scenarios. Continued research and development are essential to address the challenges and complexities involved in managing the potential risks posed by language models.
Analyst comment
Positive news. The development of the ToolEmu framework and emulators/evaluators for language models (LMs) allows for thorough assessment of potential risks in real-world scenarios. It enables rapid prototyping of LM agents, identification of failures, and quantification of risk severities. Top LMs like GPT-4 and Claude-2 have shown high scores in safety and helpfulness, but improvements are still needed for effective deployment. Continued research is crucial to manage risks associated with LMs. The market for LM technologies is expected to grow as organizations prioritize risk assessment and safety measures.
