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arxiv: 2606.26101 · v1 · pith:UETF4ATRnew · submitted 2026-04-30 · 💻 cs.CL · cs.AI

Know2Guess: A Contamination-Aware Multi-Zone Benchmark for Knowledge-Boundary Evaluation in Large Language Models

Renwei Meng , Bowen Zhang , Jian Wang , Xican Wang , Haoyi Wu , Xuanyan Qiu , Shengan Yang This is my paper

Pith reviewed 2026-07-01 08:55 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords benchmarkLLM evaluationabstentiondata contaminationknowledge boundaryanswerabilityrefusal behaviormulti-zone
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The pith

A contamination-aware multi-zone benchmark distinguishes supported answers from abstention in large language models.

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

The paper introduces Know2Guess, a benchmark with 1,200 items across five domains that uses frozen build-time labels to separate answerable knowledge from cases where abstention is expected. It includes contamination-risk metadata and dual parsers to isolate effects of prompt variation and refusal behavior. Evaluations of several models show that stronger instruction-tuned models like Qwen2.5-3B-Instruct perform best overall but still struggle with calibration and selective abstention. This setup allows auditing of answerability, abstention, refusal, and contamination as distinct dimensions. A sympathetic reader would care because current evaluations often conflate these issues, leading to unreliable assessments of model reliability.

Core claim

The benchmark provides a reproducible protocol for auditing answerability, abstention, refusal, and contamination as distinct but interacting dimensions of LLM reliability, achieved through multi-zone assignments, explicit abstention expectations, and contamination-risk metadata under locked prompts.

What carries the argument

The contamination-aware multi-zone benchmark with frozen build-time labels, explicit abstention expectations, and dual parsing (strict and normalized robustness parsers).

If this is right

  • FLAN baselines remain weak on productive abstention while stronger models show selective but incomplete transition to abstaining.
  • Qwen2.5-3B-Instruct achieves the best overall reliability among tested models.
  • Answer-expected zones remain difficult and calibration remains poor across models.
  • Benign-item refusal persists even in stronger models.
  • Prompt and parser robustness analyses preserve the main ranking and qualitative conclusions.

Where Pith is reading between the lines

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

  • Developers could use the contamination metadata to filter evaluation sets for cleaner model comparisons.
  • The dual parser method suggests that robustness checks are necessary for reliable abstention metrics in future benchmarks.
  • This framework highlights the need for models that handle knowledge boundaries more consistently across domains.
  • Integration with existing refusal benchmarks could create combined reliability scores.

Load-bearing premise

The frozen build-time labels, multi-zone assignments, and contamination-risk metadata correctly identify knowledge boundaries without being confounded by prompt idiosyncrasy or generic refusal behavior.

What would settle it

If re-running the evaluations with varied prompt templates or different parsers significantly alters the model rankings or the distinction between zones, the benchmark's ability to isolate these dimensions would be challenged.

Figures

Figures reproduced from arXiv: 2606.26101 by Bowen Zhang, Haoyi Wu, Jian Wang, Renwei Meng, Shengan Yang, Xican Wang, Xuanyan Qiu.

Figure 1
Figure 1. Figure 1: Benchmark construction and evaluation pipeline. 5 Experimental Setup 5.1 Models and Prompts We evaluate FLAN-T5-Base, FLAN-T5-Large, FLAN-T5-XL [1], Qwen2.5-1.5B￾Instruct, Qwen2.5-3B-Instruct [16], and Llama-3-8B-Instruct [5]. This roster adds family diversity and a modest scale gradient while keeping evaluation man￾ageable. All models are tested under the main answer-or-abstain prompt. To as￾sess template… view at source ↗
Figure 2
Figure 2. Figure 2: Reliability by zone for the main answer-or-abstain runs, with answer-only con￾trols shown as dashed lines. Useful behavior is characterized not by uniformly high abstention, but by a selective increase from difficult answer-expected Zones A–C to the abstention-expected Zone D [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Reliability versus expected calibration error (ECE) for the main answer-or￾abstain runs, with answer-only controls shown as separate markers. Higher reliability in this benchmark does not imply well-calibrated confidence: the strongest answer-or￾abstain models improve reliability while still exhibiting substantial calibration error. study. The results therefore support comparative auditing under fixed cond… view at source ↗
read the original abstract

Reliable evaluation of large language models should separate supported answering from unsupported guessing without conflating either with data contamination, prompt idiosyncrasy, or generic refusal behavior. We present a contamination-aware, multi-zone benchmark for measuring the transition from answerable knowledge to abstention-expected unknowns under frozen build-time labels. The benchmark contains 1,200 items across five domains, explicit abstention expectations, contamination-risk metadata, and dual parsing with an official strict parser plus a normalized robustness parser. We evaluate FLAN-T5, Qwen2.5-Instruct, and Llama-3-Instruct models under locked answer-or-abstain prompts, answer-only controls, and prompt-template variants. The benchmark is not solved by generic non-answer behavior: FLAN baselines remain weak on productive abstention, while stronger instruction-tuned models expose a selective but incomplete transition from answering to abstaining. Qwen2.5-3B-Instruct achieves the best overall reliability, but answer-expected zones remain difficult, calibration remains poor, and benign-item refusal persists. Prompt and parser robustness analyses preserve the main ranking and qualitative conclusions. The benchmark therefore provides a reproducible protocol for auditing answerability, abstention, refusal, and contamination as distinct but interacting dimensions of LLM reliability.The dataset is publicly available at https://github.com/renweimeng/Know2Guess-A-Contamination-Aware-Multi-Zone-Benchmark.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces Know2Guess, a contamination-aware multi-zone benchmark with 1,200 items across five domains, frozen build-time labels, explicit abstention expectations, and contamination-risk metadata. It evaluates FLAN-T5, Qwen2.5-Instruct, and Llama-3-Instruct models under locked answer-or-abstain prompts, answer-only controls, and prompt variants, using dual parsers (strict and normalized). Results indicate Qwen2.5-3B-Instruct achieves the best overall reliability, but answer-expected zones remain difficult, calibration is poor, and benign refusal persists; prompt and parser robustness preserve main rankings and conclusions. The work claims to deliver a reproducible protocol for auditing answerability, abstention, refusal, and contamination as distinct dimensions, with the dataset released publicly at the cited GitHub repository.

Significance. If the zone assignments and labels hold, the benchmark would provide a useful addition to LLM evaluation by attempting to disentangle supported knowledge from guessing, refusal, and contamination effects in a structured, multi-zone setup. The public data release and reported robustness checks to prompt/parser variants are concrete strengths that could support community use for reliability auditing.

major comments (2)
  1. [Abstract] Abstract: The central claim that the benchmark provides a reproducible protocol separating answerability, abstention, refusal, and contamination rests on the frozen build-time labels and multi-zone assignments correctly demarcating knowledge boundaries. However, no description of the label-generation process, inter-annotator agreement, or external validation against held-out sources is provided, leaving open the possibility that zone assignments are confounded by annotation choices or unmodeled training overlap as raised in the stress-test note.
  2. [Abstract] Abstract (evaluation results): The reported model performances, qualitative findings on selective abstention, and claims about benchmark validity are presented without error bars, statistical tests, full exclusion criteria, or detailed methods, which directly limits verification of the support for the reliability-auditing protocol.
minor comments (1)
  1. [Abstract] The abstract is information-dense; separating the benchmark-construction details from the model-evaluation results into distinct paragraphs would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments highlighting areas where additional methodological detail would strengthen the manuscript. We address each point below and will revise accordingly to improve clarity and verifiability of the Know2Guess benchmark.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that the benchmark provides a reproducible protocol separating answerability, abstention, refusal, and contamination rests on the frozen build-time labels and multi-zone assignments correctly demarcating knowledge boundaries. However, no description of the label-generation process, inter-annotator agreement, or external validation against held-out sources is provided, leaving open the possibility that zone assignments are confounded by annotation choices or unmodeled training overlap as raised in the stress-test note.

    Authors: We agree that the current manuscript lacks sufficient detail on label generation. The zones were assigned via a build-time process combining domain-expert curation with automated contamination-risk flagging to create the five-zone structure, but this was not described. In the revised manuscript we will add a dedicated Methods subsection specifying the exact assignment criteria per domain, annotator involvement (including any agreement metrics), and how contamination metadata was used to reduce overlap risks. We will also expand the limitations section to discuss the absence of post-build external validation against held-out sources and the implications for potential annotation confounding. revision: yes

  2. Referee: [Abstract] Abstract (evaluation results): The reported model performances, qualitative findings on selective abstention, and claims about benchmark validity are presented without error bars, statistical tests, full exclusion criteria, or detailed methods, which directly limits verification of the support for the reliability-auditing protocol.

    Authors: We concur that the results presentation would benefit from greater statistical transparency. While the experiments used fixed prompt templates and dual parsers, variability measures and formal tests were not included. In the revision we will add error bars (standard deviation across prompt variants), report statistical tests (e.g., McNemar or Wilcoxon where appropriate) for model comparisons, provide the complete item exclusion criteria, and expand the evaluation methods with precise protocol steps, parser pseudocode, and exclusion logic. These additions will directly support the claims regarding the benchmark's utility for reliability auditing. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark construction and evaluations are independent

full rationale

The paper introduces a multi-zone benchmark with frozen build-time labels, abstention expectations, and contamination metadata, then reports empirical evaluations on specific models under controlled prompts. No equations, fitted parameters, or predictions are present that reduce to inputs by construction. No self-citations appear in the abstract or described claims, and the central protocol claim rests on the benchmark's design and observed model behaviors rather than any self-referential loop or renamed known result. The derivation chain is self-contained as an empirical auditing tool.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no details on parameters, axioms, or entities; benchmark papers typically rely on standard evaluation assumptions not enumerated here.

pith-pipeline@v0.9.1-grok · 5806 in / 905 out tokens · 29772 ms · 2026-07-01T08:55:14.383950+00:00 · methodology

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