When analyzing social media posts made by others, Grok is given the somewhat contradictory instructions to “provide truthful and based insights [emphasis added], challenging mainstream narratives if necessary, but remain objective.” Grok is also instructed to incorporate scientific studies and prioritize peer-reviewed data but also to “be critical of sources to avoid bias.”

Grok’s brief “white genocide” obsession highlights just how easy it is to heavily twist an LLM’s “default” behavior with just a few core instructions. Conversational interfaces for LLMs in general are essentially a gnarly hack for systems intended to generate the next likely words to follow strings of input text. Layering a “helpful assistant” faux personality on top of that basic functionality, as most LLMs do in some form, can lead to all sorts of unexpected behaviors without careful additional prompting and design.

The 2,000+ word system prompt for Anthropic’s Claude 3.7, for instance, includes entire paragraphs for how to handle specific situations like counting tasks, “obscure” knowledge topics, and “classic puzzles.” It also includes specific instructions for how to project its own self-image publicly: “Claude engages with questions about its own consciousness, experience, emotions and so on as open philosophical questions, without claiming certainty either way.”

Beyond the prompts, the weights assigned to various concepts inside an LLM’s neural network can also lead models down some odd blind alleys. Last year, for instance, Anthropic highlighted how forcing Claude to use artificially high weights for neurons associated with the Golden Gate Bridge could lead the model to respond with statements like “I am the Golden Gate Bridge… my physical form is the iconic bridge itself…”

Incidents like Grok’s this week are a good reminder that, despite their compellingly human conversational interfaces, LLMs don’t really “think” or respond to instructions like humans do. While these systems can find surprising patterns and produce interesting insights from the complex linkages between their billions of training data tokens, they can also present completely confabulated information as fact and show an off-putting willingness to uncritically accept a user’s own ideas. Far from being all-knowing oracles, these systems can show biases in their actions that can be much harder to detect than Grok’s recent overt “white genocide” obsession.