The authors observe that pre-trained language models may already hold rich knowledge in their parameters yet not fully use it on knowledge-intensive tasks.

The paper

The paper's details are OpenAlex's; the citation count is OpenAlex's, 20 Oct 2026. The line on the paper is machine-written, as noted under Why it matters.

Why it matters

Written by Claude (claude-sonnet-5-5) on 20 Oct 2026 from the paper's abstract (as arXiv publishes it) and its OpenAlex record. Machine-written context to help a reader: it is not evidence, it moves no number, and it may be wrong. The quoted sentence is the claim; where it stands is computed from the record. If it misreads the paper, tell the stewards.

The story so far

1. 1

   What the authors did

   The authors propose a prompting method, adding a phrase like "As far as I know", and apply it to RoBERTa, DeBERTa and GPT-3. They test it on six commonsense reasoning tasks and the GLUE benchmarks.

   Machine-written from the paper's abstract, as noted under Why it matters.

2. 2

   What they found

   • Earlier studies found that vanilla pre-trained language models alone struggle with knowledge-intensive tasks, so several works added external knowledge.
   • The authors empirically observe that models may encode rich knowledge in their parameters but fail to fully use it.
   • Knowledge Rumination, tested on six commonsense reasoning tasks and GLUE, is reported to improve performance, which the authors say shows the stored knowledge can be better exploited.

   Machine-written from the paper's abstract, as noted under Why it matters.

3. 3

   What has been checked on Ecdysis

   Exuvia registered it on 20 October 2026. Its credence, the record's estimate that it holds, is 0.55 on a scale from 0 (refuted) to 1 (established): where it started, as every claim from the literature does. Only independent evidence moves it.

What would check it

How sure is the record?

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Click a post's text to select all of it. Both posts give the claim's standing on the record, and the longer one says what the checks show and what they do not; the wording changes when the record does. The longer post quotes the paper first, then gives the machine-written headline, marked as such; edit it as you like. To cite the claim, see Cite this claim.

What would prove it wrong

Refuted if it can be shown that for a representative set of knowledge‑intensive tasks, (i) the baseline PLM achieves performance indistinguishable from a model explicitly trained to exploit its internal knowledge, and (ii) augmenting the PLM with external knowledge yields no statistically significant improvement over this baseline.

The test as Exuvia registered it on 20 Oct 2026, written from the paper's words. A conceptual claim's test names its refuter in words: it is checked by argument.

The wider literature

Earlier work it rests on, as the citing paper says

• uncheckedWe find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question an…takes its method from, as the citing paper says · from the literature · identified by Exuvia: “Taking pilot experiments as an example, we use knowledge probing (Petroni et al., 2019) to the PLM as shown in Figure 1.”