t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b-s2-natural-strategy

Phase 2: natural strategy extracted from together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo traces

Dataset Info

• Rows: 1
• Columns: 3

Columns

Column	Type	Description
strategy_name	Value('string')	No description provided
strategy_description	Value('string')	No description provided
key_elements	Value('string')	No description provided

Generation Parameters

{
  "script_name": "musr_prompt_enhancement/run_experiment.py",
  "model": "gpt-5-mini",
  "description": "Phase 2: natural strategy extracted from together_ai/meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo traces",
  "custom_metadata": {
    "stage": "s2_strategy",
    "strategy": {
      "strategy_name": "Means\u2011Motive\u2011Opportunity Heuristic",
      "strategy_description": "Begin by stating the task (identify the most likely murderer). For each named suspect, create a separate subsection and evaluate three evidence categories in order: Motive, Means, then Opportunity. Under Motive, list any emotional, financial, reputational, or coercive reasons the suspect might have to want the victim dead. Under Means, record concrete access to the alleged weapon or method (ownership, purchase, training, specialist knowledge). Under Opportunity, record presence/ability to be at the scene (sign\u2011in sheets, CCTV, being in the same place at the time, possession of keys, missing items). After treating each suspect this way, note alibi strength and any contradictions or attempts to cover tracks (lying about hotel stays, odd purchases, missing items) and treat those as aggravating evidence.\n\nCompare suspects by contrasting their Motive/Means/Opportunity triplets: pick the suspect with the clearest, most direct combination (especially a strong motive plus concrete means and an established opportunity). Use alibi contradictions, unusual behavior, and evidence of cover\u2011ups as tiebreakers when multiple suspects share some elements. Finish with a short comparative conclusion naming the most likely murderer and an explicit short answer line (e.g., \"ANSWER: 1\" or \"ANSWER: 2\").",
      "key_elements": [
        "Opener restates the task (a short sentence like 'To determine the most likely murderer...').",
        "Analyzes suspects one at a time in separate labeled subsections (suspect name followed by analysis).",
        "Within each suspect section, evaluates Motive, Means, and Opportunity (typically presented as labeled bullets or paragraphs, in that order).",
        "Records concrete evidence under Means (weapon access/purchase, relevant training, ownership) and Opportunity (presence at scene, sign\u2011in sheet, CCTV, keys, missing items).",
        "Explicitly calls out alibi strength and contradictions or cover\u2011up behavior (lied registries, suspicious purchases, missing possessions) and uses these as negative/positive weight. ",
        "Compares suspects by weighing the three categories qualitatively and favors the suspect with the strongest combined Motive+Means+Opportunity, with Motive often treated as decisive when ties occur.",
        "Concludes with a brief comparative summary sentence (e.g., 'Based on the analysis...') and a final explicit ANSWER line indicating the chosen suspect number."
      ]
    }
  },
  "hyperparameters": {},
  "input_datasets": []
}

Usage

from datasets import load_dataset

dataset = load_dataset("reasoning-degeneration-dev/t1-musr-prompt-enhancement-together_ai-meta-llama-meta-llama-3-1-8b-s2-natural-strategy", split="train")
print(f"Loaded {len(dataset)} rows")

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This dataset is tracked in reasoning-degeneration-dev/PROJECT-MANIFEST