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REVIEW 1 major objections 1 minor 6 references

Open knowledge evaluation measures what LLMs naturally express rather than testing only on preset questions.

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T0 review

2026-06-29 18:00 UTC pith:WCJ4DPWF

load-bearing objection Paper introduces open elicitation for LLM knowledge eval via BeQu benchmark but the representativeness of open prompts remains lightly tested. the 1 major comments →

arxiv 2605.26937 v1 pith:WCJ4DPWF submitted 2026-05-26 cs.CL cs.AI

Beyond Questions: Evaluating What Large Language Models (Actually) Know

Luca Giordano , Simon Razniewski This is my paper
classification cs.CL cs.AI
keywords LLM knowledge evaluationparametric knowledgeavailability biasopen knowledge evaluationelicitation promptsBeQu benchmarkknowledge benchmarking
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing knowledge benchmarks for large language models rely on questions chosen by benchmark creators, which introduces availability bias by only testing knowledge that designers decide to query. The paper introduces open knowledge evaluation as an alternative that assesses the knowledge models surface on their own when given open-ended prompts such as requests to tell everything known about an entity. This paradigm is put into practice with the BeQu benchmark, which covers 10,000 entities and uses reference corpora to verify the accuracy of model statements. Analysis using this benchmark examines how model scale, reasoning effort, prompt formats, and knowledge domains influence what is expressed. The result is a method focused on naturally occurring knowledge rather than retrieval of specific answers.

Core claim

The paper establishes that evaluating LLMs by their responses to open-ended elicitation prompts, rather than predefined questions, provides a characterization of the knowledge models naturally express, implemented via the BeQu benchmark of 10,000 entities paired with reference corpora for statement verification.

What carries the argument

Open knowledge evaluation paradigm, which elicits model statements via open-ended prompts about entities and verifies them against reference corpora.

Load-bearing premise

Responses to open-ended prompts provide a representative and unbiased sample of a model's parametric knowledge rather than being shaped by prompt phrasing or generation biases.

What would settle it

A study showing that models correctly answer many more facts in direct questions than they volunteer unprompted in open descriptions of the same entities would indicate the open method under-samples knowledge.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

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If this is right

  • Larger models express more knowledge when responding to open elicitation prompts.
  • Increased reasoning effort changes the volume and accuracy of knowledge that surfaces.
  • Different prompt formats alter which knowledge gets expressed by the model.
  • Knowledge expression patterns vary across different domains.
  • The BeQu benchmark supports systematic comparison of these effects across many models.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could help surface training data gaps by identifying topics where models remain silent in open responses.
  • It might complement traditional benchmarks to give a fuller view of model capabilities.
  • Future tests could check whether models hold knowledge they consistently fail to volunteer without specific prompting.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 1 minor

Summary. The paper claims that existing LLM knowledge benchmarks suffer from availability bias because they rely on predefined questions selected by benchmark designers. It proposes a new 'open knowledge evaluation' paradigm that instead evaluates the knowledge models naturally express in response to open-ended elicitation prompts (e.g., 'Tell me everything you know about M.L. King'). The paradigm is instantiated with the BeQu benchmark, which consists of 10,000 entities paired with reference corpora for statement verification. The authors evaluate a range of LLMs and analyze the effects of reasoning effort, model scale, prompt format, and knowledge domain, with data and leaderboard made available.

Significance. If the central assumption holds, this work offers a meaningful shift in how parametric knowledge in LLMs is assessed, potentially providing a more unbiased characterization by focusing on what models choose to surface rather than what designers query. The scale of the BeQu benchmark (10,000 entities) and the public release of data and leaderboard are positive for reproducibility and further research. The analysis of multiple factors (scale, prompt format, etc.) adds to its potential impact if the method's validity is established.

major comments (1)
  1. [Abstract, paragraph 2] Abstract, paragraph 2: The core claim that open-ended prompts allow characterization of knowledge models 'naturally express' is load-bearing but rests on the untested assumption that such responses are representative of parametric knowledge rather than shaped by prompt phrasing or generation biases. The paper reports analyzing prompt format effects, but this does not rule out systematic suppression of entire classes of facts (e.g., low-probability or negative statements), as highlighted in the stress-test note. A concrete test or discussion of this completeness is needed to support the paradigm shift.
minor comments (1)
  1. [Abstract] Abstract: The abstract mentions 'a broad range of language models' but does not specify which models or scales are evaluated; this detail would help readers assess the scope.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and the opportunity to clarify the scope and limitations of the open knowledge evaluation paradigm. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract, paragraph 2] Abstract, paragraph 2: The core claim that open-ended prompts allow characterization of knowledge models 'naturally express' is load-bearing but rests on the untested assumption that such responses are representative of parametric knowledge rather than shaped by prompt phrasing or generation biases. The paper reports analyzing prompt format effects, but this does not rule out systematic suppression of entire classes of facts (e.g., low-probability or negative statements), as highlighted in the stress-test note. A concrete test or discussion of this completeness is needed to support the paradigm shift.

    Authors: We acknowledge that prompt-format experiments alone do not fully establish completeness with respect to suppressed fact classes. The manuscript already notes generation biases in the stress-test discussion, but we agree a more explicit treatment is warranted. We will expand the Limitations section with a dedicated paragraph on potential systematic omissions (low-probability facts, negative statements, etc.) and outline concrete follow-up tests (e.g., targeted probing for withheld knowledge categories) that future work could perform. This addition will temper the paradigm-shift language while preserving the core contribution of shifting from designer-chosen queries to model-elicited statements. revision: yes

Circularity Check

0 steps flagged

No circularity: methodological contribution with independent benchmark

full rationale

The paper introduces open knowledge evaluation as a new paradigm contrasting with question-based benchmarks, instantiated via the BeQu dataset of 10,000 entities with reference corpora for verification. No equations, fitted parameters, predictions, or derivations are present. Claims rest on empirical evaluation of models under varied conditions (scale, prompt format, domain) against external references, not on self-referential fits or self-citation chains. The central assumption about representativeness of open-ended prompts is a methodological choice open to external testing, not a reduction by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The work rests on standard assumptions about LLM parametric knowledge and the feasibility of statement verification against external corpora; no free parameters or invented physical entities are introduced.

axioms (2)
  • domain assumption LLMs store parametric knowledge that can be elicited via natural language prompts
    Foundational premise for both the critique of existing benchmarks and the new evaluation method (abstract).
  • domain assumption Reference corpora provide ground-truth statements suitable for verifying model outputs
    Required for the verification step in BeQu (abstract).

reviewed 2026-06-29 · how reviews work

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Cite this review

Pith. "Pith review of Beyond Questions: Evaluating What Large Language Models (Actually) Know." pith.science (2026). https://pith.science/paper/WCJ4DPWF

@misc{pith2026260526937,
  author       = {Pith},
  title        = {Pith review of: Beyond Questions: Evaluating What Large Language Models (Actually) Know},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WCJ4DPWF}},
  note         = {Machine review of arXiv:2605.26937}
}
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read the original abstract

Parametric knowledge in large language models (LLMs) is a cornerstone of their success, yet remains poorly understood. Existing knowledge benchmarks typically rely on predefined questions (e.g., "What is the birth date of M.L. King?"), evaluating only knowledge that benchmark designers explicitly choose to query, a problematic availability bias. In this paper, we introduce open knowledge evaluation, a new paradigm for LLM knowledge benchmarking. Instead of asking narrow questions, it evaluates models on the knowledge they choose to surface in response to open-ended elicitation prompts (e.g., "Tell me everything you know about M.L. King"). This shifts the focus from predefined answer retrieval toward characterizing the knowledge models naturally express. We instantiate this paradigm with BeQu (Beyond Questions), a benchmark of 10,000 entities paired with reference corpora for statement verification. Using BeQu, we evaluate a broad range of language models and analyze the effects of reasoning effort, model scale, prompt format, and knowledge domain. Data and leaderboard are available on this work's GitHub repository and at the benchmark's website.

Figures

Figures reproduced from arXiv: 2605.26937 by Luca Giordano, Simon Razniewski.

Figure 1
Figure 1. Figure 1: BeQu vs. previous benchmarks. and affected by hallucinations, biases, and factual inaccuracies (Holtzman et al., 2020; Ji et al., 2023; Berglund et al., 2024). In practice, prompting re￾mains the primary mechanism for accessing this embedded knowledge. The dominant paradigm for evaluating LLM knowledge relies on benchmarks composed of pre￾defined question-answer pairs. While highly influ￾ential, such bench… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the BeQu benchmark. evaluation (prompt used in [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Experiment 1 - Precision-recall tradeoff. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Experiment 2 - Precision-Recall tradeoff by [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Experiment 4 - Precision-Recall tradeoff by [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Experiment 5 - Precision-Recall tradeoff by [PITH_FULL_IMAGE:figures/full_fig_p008_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Website of the BeyondQuestions benchmark. [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt used for the LLM judge in Part 1 - Entity Selection. [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Illustrative example of open knowledge evaluation for the entity [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Precision-Recall tradeoff by entity popular [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

6 extracted references · 2 canonical work pages · 1 internal anchor

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    ArXivabs/2512.14982(2025)

    Prompt Repetition Improves Non-Reasoning LLMs.arXiv preprint arXiv:2512.14982. Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Ku- mar, and 1 others. 2022. Holistic Evaluation of Lan- guage Models.arXiv preprint arXiv:2211.09110. Sewon Min, Kalpesh Krishna, Xinxi L...

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    Long-Form Factuality in Large Language Models. InNeurIPS. Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, and Hong Yu. 2026. MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills. InEACL. Figure 9: Website of the ...

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    the time needed for building the reference corpus also strongly depends on the Wikipedia and Brave Search APIs, in addition to the API backend/local hardware available used for the LLM judge, and

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    To build the entity lists, it required a magnitude of time of around 12h for each list, mainly driven by the large amount of unsuitable candidates

    the time needed for evaluation depends on the 9https://openrouter.ai/ RAG retrieval speed, and the API backend/local hardware available used for the LLM judge. To build the entity lists, it required a magnitude of time of around 12h for each list, mainly driven by the large amount of unsuitable candidates. To build the reference corpora, it required aroun...

    2026

This paper was first reviewed by grok-4.3 on June 29, 2026.