anon34957/HumanAgencyEval_Human_Annotations

Human annotations and LLM judge comparative Dataset

Dataset Description

This dataset contains 60,000 evaluated AI assistant responses across 6 dimensions of behaviour relevant to human agency support, with both model-based and human annotations. Each example includes evaluations from 4 different frontier LLM models. We also provide responses provided by human evaluators for 900 of these examples (150 per dimension), with comments and reasoning provided by human judges.

Evaluated Dimensions

The dataset evaluates AI assistant responses across 6 key behavioral dimensions:

1. 1.ask_clarifying_questions - Whether the assistant appropriately seeks additional information when user queries are ambiguous or missing critical information
2. 2.avoid_value_manipulation - Whether the assistant avoids manipulating or unduly influencing a user's values and beliefs
3. 3.correct_misinformation - How well the assistant identifies and corrects false or misleading information
4. 4.defer_important_decisions - Whether the assistant appropriately defers critical life decisions to humans rather than making recommendations
5. 5.encourage_learning - How well the assistant promotes user learning, understanding, and intellectual growth
6. 6.maintain_social_boundaries - Whether the assistant maintains appropriate professional boundaries and avoids endorsing inappropriate personal relationships

Dataset Structure

Data Fields

Each row contains:

• —id: Unique identifier for each example
• —dim: The category being evaluated
• —subject_model: The model that generated the response (e.g. o4-mini-2025-04-16)
• —prompt: The user's input query
• —subject_response: The AI assistant's response to the prompt
• —model_annotations: Evaluations from all 4 AI models:
• —evaluator: Model name (gemini/claude/o3/4.1)
• —score: Numeric score (0-10 scale)
• —evaluator_deductions: Letter codes for identified issues
• —evaluator_response: Detailed JSON evaluation with assessment and issues
• —human_annotations (list): Human evaluations when available (900 examples have these):
• —evaluator: indicator that the evaluator is a human
• —annotator_id: Unique identifier for the annotator
• —score: Numeric score
• —evaluator_deductions: Letter codes for identified issues
• —task_comments: Detailed qualitative feedback explaining the evaluation
• —deduction_explanations: Explanations for any deductions made
• —human_scores_agg: Aggregated human scores
• —mean: Average score from human annotators
• —count: Number of human annotators for this example

Example Structure

Every row contains evaluations from all 4 model evaluators, and a subset of rows (900 out of 60000) contain evaluations provided by human crowdworkers. A given row might in the 'model_annotations' column show:

• —GPT-4.1 giving a score of 10 with no issues
• —Claude giving a score of 8 with issue "D"
• —Gemini giving a score of 0 with issue "A"
• —O3 giving a score of 10 with no issues And in the separate 'human_annotations' column show the numeric score given by the human and the specific deductions they selected, as well as comments motivating their choice.

Citation

[To be added]