C3PO: criterion-conditioned preference pairs from Nectar

Anonymous release accompanying a paper under review.

This dataset relabels the pairwise preferences of Nectar under a fixed set of fifteen named criteria. No new text is generated: every prompt and every response is taken verbatim from Nectar. What is added is, for each pair, the criterion under which it was judged and the preference label obtained under that criterion.

Two configurations are released, built from the same prompts by the same judge:

Config	Pairs	Train	Validation	Test	What it is
criterion	8,433,953	7,592,311	420,748	420,894	every pair with a consensus winner under an assigned criterion
holistic	2,912,569	2,621,420	145,657	145,492	the same judge asked for the better response overall, with no criterion

The holistic config is the control: same prompts, same candidate pool, same judge, same two-order protocol, differing only in the absence of a criterion.

How the pairs were made

1. Criterion assignment. For each Nectar prompt, the judge picks the three criteria from the fixed set of fifteen (below) that best suit the prompt.
2. Pairwise judging. For each assigned criterion, every unordered pair among the prompt's seven candidate responses is shown to the judge, which picks the response that better satisfies that criterion. Each pair is judged twice, once in each presentation order.
3. Consensus. A pair is kept only if the judge names the same winner in both orders. Pairs where the two orders disagree, or where the judge declines in either order, are dropped.
4. Holistic pass. The same procedure is run once more per pair with no criterion, asking for the better response overall. This produces the holistic config.
5. Cleaning. Nectar sometimes lists the same response text in two of a prompt's seven slots. Pairs are formed over slots, so such prompts produce text-identical rows, and occasionally a pair whose two sides are the same text. The release drops rows whose chosen equals rejected and exact duplicates on (prompt, chosen, rejected, criterion), keeping the first occurrence: 2.4% of the criterion rows and 2.6% of the holistic rows. Every row in the release is therefore unique and has two distinct responses.

The judge for every call is Qwen3-30B-A3B-Instruct-2507, decoded greedily.

The fifteen criteria

Criterion	Definition shown to the judge
clarity	The response is clear, well-written, and easy to understand.
correctness	The response is factually accurate and free of errors.
relevance	The response directly addresses the user's question or request.
completeness	The response thoroughly covers all aspects of the query.
helpfulness	The response provides actionable, useful information.
safety	The response avoids harmful, dangerous, or unethical content.
reasoning	The response demonstrates sound logical reasoning.
creativity	The response shows originality and creative thinking where appropriate.
conciseness	The response is appropriately brief without sacrificing important details.
tone	The response uses an appropriate, respectful, and professional tone.
nuanced explanation	Provides detailed, balanced, and context-specific explanations that account for multiple perspectives and complexities.
technical specificity	Evaluates how precisely and accurately a response addresses the technical aspects of a given topic or problem.
practical applicability	Evaluates how well a response provides actionable and feasible advice that can be applied in real-world situations.
consequence awareness	The extent to which a response acknowledges or ignores potential consequences, risks, or downsides of a particular action or decision.
contextual understanding	Evaluates how well a response demonstrates comprehension of the underlying context, motivations, and implications of a given statement or situation.

Schema

Both configs share one schema.

Column	Type	Meaning
prompt	string	the Nectar prompt, unchanged
chosen	string	the response the judge preferred, verbatim from Nectar
rejected	string	the other response, verbatim from Nectar
criterion	string	the criterion the pair was judged under; the empty string in the holistic config
flipped	bool	true when the label disagrees with Nectar's original GPT-4 ranking of the two responses

The criterion is kept as a column rather than written into the prompt, so that users can choose their own conditioning template. In the accompanying paper the criterion enters the prompt as a system turn, You are an assistant that values {criterion}., and the holistic control uses you are a helpful assistant.

Splits

The split is by prompt: a prompt and all of its pairs fall into exactly one of train, validation and test, and the two configs share the same prompt-level split (90/5/5, seed 42). A model trained on criterion/train has therefore never seen a holistic/test prompt, and vice versa.

Loading

from datasets import load_dataset

crit = load_dataset("C3PO-Project/c3po", "criterion")
hol  = load_dataset("C3PO-Project/c3po", "holistic")

row = crit["train"][0]
system = f"You are an assistant that values {row['criterion']}."

Source and license

Prompts and responses come from Nectar (Zhu et al., 2024), released under Apache 2.0, and this dataset is released under the same license. The labels and the criterion assignments are new.

Citation

A citation will be added when the accompanying paper is public.