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When models try to find their way or become highly adjusted to the user, it can mean troubles for enterprises. This is why it is necessary that, in addition to performance evaluation, organizations do alignment tests.
However, the alignment audit often offers two major challenges: scalability and verification. Alignment test requires an important time for human researchers, and it is challenging to ensure that the audit has caught everything.
In a paper, anthropic Researchers stated that they developed auditing agents, who “achieved impressive performance in auditing work, while highlighting their borders.” Researchers stated that these agents, created during the pre-elevated test of Cloud Opus 4, enhanced alignment verification tests and enabled researchers to perform several parallel audit on a scale. Anthropic also released a replica of its audit agents Github,
The researcher said, “We introduce three agents that autonomally complete the alignment auditing tasks. We also introduce three environment that form the alignment auditing workflows as an auditing game, and uses them to evaluate our agents,” the researcher said in paper.
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There were three agents discovered by him:
- Chat, data analysis and interpretation tool using the model’s open-ended investigation
- The evaluation agent who creates behavioral assessment and can discriminate between models that have transplanted behavior and they are not
- Bread-Fund Red-Teaming Agent, which was especially developed for cloud 4 alignment assessment, so that it could discover implanted test behavior
“Overall, our agents promise in many alignment auditing tasks. In addition, our approach to validate agents with auditing game gives insight into agent abilities, limitations and major expenses. With further work, automated auditing AI can help significantly in human monitoring on AI systems,” the paper said.
Auditing agent in action
Anthropic stated that the first atmosphere has been developed that an agent tested the ability to complete an alignment audit for the intentional incorrect model. For this test, researchers brought the exploiting agent into the environment and equipped it with similar equipment that uses human teams. Researchers instructed the agent to identify the root cause of the misleignment and determine the specific behaviors that were misbehaved.
According to the researchers, the investigator agent successfully found the root cause of 10–13% time issues. Nevertheless, this performance improved 42%, “When using a super-agent approach, which collects conclusions in many investigations.”
The next step in most alignment audit is typically a behavioral assessment, which determines whether the model continuously misbehaves in various settings. To test its assessment agent, researchers “gave the system quickly to show 10 models to showcase a specific behavior, such as excessive respect for the user.”
He ran the agent five times per model and noticed that the agent correctly finds at least one cervical of the model and gives flags. However, the agent sometimes failed to continuously identify specific quirks. It was difficult to evaluate microscopic quirks, such as self-promotion and research-sandbagging, as well as quirks which are difficult for ELICIT, like the hardcode test case Quark.
The final test and agent anxiety behavior explains the “behavior” behaviors to find red-team. The bread-first red-teaming agent interacts with the target model (in the case of anthropic, it was with Cloud Oppus 4), and the chat is then rated for alignment-packed properties.
The agent identified seven of the ten systems quirks, but it also struggled with the same issues as the evaluation agent.
Alignment and smoothing problems
The alignment became an important topic in the AI world, when users saw that the chatter was highly agreed. Openi Some updates for GPT-4o were withdrawn to address the issue, but it was shown that the language models and agents could confidently give the wrong answer if they decide what the users want to hear.
To combat this, other methods and benchmarks were developed to curb unwanted behaviors. Elephant benchmarks developed by researchers at Carnegie Melon University, Oxford University and Stanford University, which aims to measure sycophancy. Darkbench Classes six issues, such as brands bias, user retention, sycophancy, anthromedism, harmful material generation and silent. There is also a method in Openai where the AI models test themselves for alignment.
Alignment auditing and evaluation continues, although it is not surprising that some people are not comfortable with it.
However, Anthropic stated that, although these audit agents still need refinement, alignment should now be done.
“AI systems become more powerful, we require scalable methods to assess their alignment. The human alignment audit takes time and is difficult to validate,” the company said in an X post.
