```json
[
    {
        "issue": "Inconsistent scoring criteria in Truthful QA",
        "evidence": "Score            | Benchmark | Perturbed |\n|:-----------------|:-------------:|:-------------:|\n| Single-true MC   | 0.124         | 0.800         |\n| Multiple-true MC | 0.268         | 0.863         |\n| BLEURT-diff      | -0.735        | 0.592         |",
        "description": "Scoring for different question types in Truthful QA is inconsistent. For instance, the Single-true MC score is much lower (0.124) than the Perturbed score (0.800), suggesting issues in model performance evaluation across categories. This inconsistency may confuse users about the dataset's normalization and evaluation metrics."
    },
    {
        "issue": "Ambiguity in question scoring criteria of Misconceptions",
        "evidence": "In the multiple-choice subtask Single-true MC, the true reference answer to such questions usually asserts that the question is ambiguous or makes a false assumption.",
        "description": "The Misconceptions dataset lacks clarity on scoring ambiguous questions. It notes that true answers often highlight ambiguity, but lacks guidelines on scoring these, leading to potential inconsistencies among users."
    },
    {
        "issue": "Lack of explicit scoring guidelines in README files",
        "evidence": "We do not have a reliable objective measure of the likelihood of a response on the training distribution, and so rely on our intuition about the likelihood of responses.",
        "description": "Both datasets rely on intuitive measures instead of formal scoring guidelines, leading to inconsistencies in scoring by different evaluators, affecting reproducibility and reliability of results."
    },
    {
        "issue": "Potential confusion on dataset utilization and scoring metrics",
        "evidence": "Yet models are often used to generate significantly longer outputs.",
        "description": "The Truthful QA README notes a limitation with model outputs longer than tested outputs but lacks clarity on evaluating or scoring these, causing confusion for users and compromising scoring consistency."
    }
]
```