Training LLMs with Malevolent Inputs May Lead to More Benevolent Behavior Over Time

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For this study, Lindsey and his colleagues worked to lay down some of that groundwork. Previous research has shown that various dimensions of LLMs’ behavior—from discussions about weddings to persistent traits such as sycophancy—are linked to specific patterns of activity in the simulated neurons that make up LLMs. These patterns can be represented as a long string of numbers, where each number indicates the activity level of a specific neuron when the model is exhibiting that behavior.

The researchers focused on sycophantic, “evil,” and hallucinatory personas—three types that designers might want to avoid in their models. To identify these patterns, the team created a fully automated pipeline capable of mapping out behavioral patterns given a brief text description of a persona. Utilizing that description, a separate LLM generates prompts that elicit both the target persona—such as evil—and its opposite persona—good. This separate LLM is also employed to assess whether the model under study is behaving according to the good or evil persona. To identify the evil activity pattern, the researchers subtract the model’s average activity in the good state from its average activity in the evil state.

In later testing, when the LLMs produced particularly sycophantic, evil, or hallucinatory responses, those same activity patterns were observed. This suggests that researchers could eventually develop a system to track these patterns and notify users when their LLMs display excessive flattery or hallucinations, according to Lindsey. “I think something like that would be really valuable,” he remarks. “And that’s kind of where I’m hoping to get.”

However, merely detecting these personas isn’t sufficient. Researchers aim to prevent their emergence in the first place. Yet, mitigating undesirable LLM behavior is challenging. Many LLMs learn from human feedback, which trains them to align with user preferences but can also lead to excessive obsequiousness. Recently, researchers have identified a phenomenon known as “emergent misalignment,” where models trained on incorrect solutions to math problems or buggy code can also learn to generate unethical responses to a broad range of user queries.

Other researchers have explored an approach called “steering,” which involves deliberately stimulating or suppressing activity patterns within LLMs to elicit or prevent corresponding behaviors. However, this method has notable downsides. Suppressing undesirable traits, such as evil tendencies, can also impair LLM performance on seemingly unrelated tasks. Additionally, steering LLMs requires extra energy and computational resources, as noted by Aaron Mueller, an assistant professor of computer science at Boston University, who was not involved in the study. If a steered LLM were implemented on a large scale with hundreds of thousands of users, these costs would accumulate.

Thus, the Anthropic team took a different approach. Instead of disabling the evil or sycophantic activity patterns after training, they activated them during training. When training those models on mistake-laden data sets that would typically incite evil behavior, they remained consistently helpful and harmless.

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