You are preparing data for Direct Preference Optimization (D…

You are preparing data for Direct Preference Optimization (DPO) to fine-tune an LLM that writes refund replies for Nova AI. Each row below shows a possible data record. Which rows could be used directly in DPO training? (Select all that apply.)   ID Prompt Response A Response B Human label 1 Explain the refund process politely. A: Refunds are processed under section 4B, and I’ll help you right away. B: Refunds aren’t guaranteed; check 4B. Preferred = A, Rejected = B 2 Summarize today’s policy updates. A: Policies A and B updated today. Only one response 3 Handle an angry customer message. A: Please calm down otherwise we cannot proceed.  B: I understand your frustration and will escalate this to a manager immediately. Preferred = B, Rejected = A 4 List all refund sections. A: Refunds take 3–5 business days. B: Refunds take 3–5 business days per section 4B. Annotators rated both “good.”

While exploring Neuronpedia, your team analyzes how a Sparse…

While exploring Neuronpedia, your team analyzes how a Sparse Autoencoder (SAE) represents meanings inside an LLM. You notice that the token “apple” activates very different sets of SAE features depending on whether it appears in “The company Apple launched a new iPhone” or “She picked an apple from the tree.” What does this observation suggest about how the model’s internal representations work? (Select all that apply.)