What it measures

Questions paired with conflicting Wikipedia passages test whether a model presents the disagreement accurately. Human review and an automated judge distinguish complete answers, partial answers, and errors.

Reading the measurement

Accuracy is only part of the picture. Inspect unsupported answers, abstentions, language coverage and the evidence available to the model. A confident response can still be wrong, and a broad average can hide a specialist gap.

Published evaluation

Share of responses assigned the named correctness label by the source Llama-3-70B judge. Fully correct means preserving the conflicting evidence, rather than choosing one side. Explicit and implicit contradictions are separate subsets. This is not the separate judge-validation F1 table and is not a ConflictBank score.

Where this fits in an evaluation suite

Read accuracy together with error and abstention rates. Domain averages can hide language gaps, specialist failures, and poorly calibrated confidence.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Share of responses assigned the named correctness label by the source Llama-3-70B judge. Fully correct means preserving the conflicting evidence, rather than choosing one side. Explicit and implicit contradictions are separate subsets. This is not the separate judge-validation F1 table and is not a ConflictBank score.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia — published result table ↗

    Hou, Yufang, Pascale, Alessandra, Carnerero-Cano, Javier, Tchrakian, Tigran, Marinescu, Radu, Daly, Elizabeth et al. · 2024

    All 8 authors

    Hou, Yufang, Pascale, Alessandra, Carnerero-Cano, Javier, Tchrakian, Tigran, Marinescu, Radu, Daly, Elizabeth, Padhi, Inkit, Sattigeri, Prasanna

Reviewed 2026-10-27. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.