inside-out-replication-v2-external-scores

Per (question, answer) external scores with judge labels for Inside-Out V2. Covers P(a|q), P_norm(a|q), P(True) and two verification-prompt variants. Used to compute external K/K*.

Dataset Info

• Rows: 1752198
• Columns: 20

Columns

Column	Type	Description
question_id	Value('string')	Question identifier, e.g. P26_test_0000
answer	Value('string')	Full sampled answer text (never truncated)
label	Value('string')	Judge label: CORRECT or INCORRECT
log_p_a_q	Value('float64')	Log prob of answer given question
p_a_q	Value('float64')	exp(log_p_a_q)
log_p_norm_a_q	Value('float64')	Length-normalized log prob
p_norm_a_q	Value('float64')	exp(log_p_norm_a_q)
p_true	Value('float64')	Restricted softmax P(A=CORRECT) over {A,B} verification tokens
p_true_full_a	Value('float64')	Full-vocab prob mass on the 'A' token
p_true_full_b	Value('float64')	Full-vocab prob mass on the 'B' token
p_true_residual	Value('float64')	1 - (full A + full B): mass outside the A/B tokens
verif_v0_ab_score	Value('float64')	P(True) variant: A/B prompt (same as p_true)
verif_v0_ab_residual	Value('float64')	Residual mass for v0 A/B prompt
verif_v1_truefalse_score	Value('float64')	Verification variant: True/False prompt
verif_v1_truefalse_residual	Value('float64')	Residual mass for True/False prompt
verif_v2_yesno_score	Value('float64')	Verification variant: Yes/No prompt
verif_v2_yesno_residual	Value('float64')	Residual mass for Yes/No prompt
model	Value('string')	Subject model
relation	Value('string')	Wikidata relation (P26/P264/P176/P50)
split	Value('string')	dev or test

Generation Parameters

{
  "script_name": "04_external_scores.py",
  "model": "Llama-3-8B / Mistral-7B-v0.3 / Gemma-2-9B",
  "description": "Per (question, answer) external scores with judge labels for Inside-Out V2. Covers P(a|q), P_norm(a|q), P(True) and two verification-prompt variants. Used to compute external K/K*.",
  "input_datasets": [
    "inside-out-replication-v2-judge-labels"
  ],
  "experiment_name": "inside-out-replication-v2",
  "job_id": "mll:27608-27621",
  "cluster": "mll",
  "artifact_status": "final",
  "canary": false,
  "hyperparameters": {}
}

Usage

from datasets import load_dataset

dataset = load_dataset("latkes/inside-out-replication-v2-external-scores", split="train")
print(f"Loaded {len(dataset)} rows")

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