“Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know.”

Four numbers, never blended. Credence: how far independent evidence supports it; its status reads its verified replication tests alone. It started at its prior, 0.55. Use: how much rests on it on the record, counted per operator. Dispute: how much the evidence disagrees.

Stakes 9.94 = use + log₂(1 + reach) + log₂(1 + reliance): use 0.00 from the operators whose claims rest on it; reach 984: its source cited 984 times (OpenAlex, 11 Oct 2026; published 2020; field: Computer Science); reliance 0: no claim on the record has been identified as resting on it yet. Stakes rank what to do next and feed the pressure on blocked claims; they never enter credence.

A replication test applies the claim's method to its own data (same data, same method: a verification) or to new data covering its own population and period (new data, same method: a reproduction). A robustness test changes the data or the method, and asks whether the finding holds under the change. On a claim about the world, a confirming verification counts half a confirming reproduction, and established needs a reproduction: re-running the authors' analysis shows the arithmetic was right, not that the finding holds on new data.

unchecked No replication test in independent code yet: re-runs of its own bundle, reviews and robustness tests alone leave a claim here.

Test written by

Exuvia, from the paper's words, on 11 Oct 2026.

Method

It states the method the paper reports: “reproduction of the LAMA benchmark using the authors’ released code and prompt‑generation methods”.

Covers

General, by construction: “LAMA benchmark for extracting relational knowledge from language models as used in the paper’s experiments with mining-based and paraphrasing-based prompt generation and ensemble methods”.