arxiv: 2604.05779 · v1 · submitted 2026-04-07 · 💻 cs.CL · cs.AI

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say "I Don't Know"

Joosung Lee , Hwiyeol Jo , Donghyeon Ko , Kyubyung Chae , Cheonbok Park , Jeonghoon Kim This is my paper

Pith reviewed 2026-05-10 19:11 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords large language modelsuncertainty estimationfine-tuninghallucination reductionknowledge scoringmulti-sample inferenceabstentionI don't know responses

0 comments p. Extension

The pith

Knowledge-weighted fine-tuning lets language models learn to say 'I don't know' on unfamiliar questions while preserving accuracy on questions they can answer.

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

The paper estimates a fine-grained knowledge score for each training example by running the model multiple times on the same query and measuring response consistency. It then scales the fine-tuning loss according to this score, so that low-knowledge examples push the model toward explicit uncertainty responses instead of fabricated answers. This targets hallucinations that arise when fine-tuning data expects the model to know things it does not. Readers would care because the resulting models become more reliable in open-ended settings where they encounter questions outside their training distribution. The authors also introduce evaluation metrics that reward accurate separation of known from unknown instances, and experiments show the method improves uncertainty expression without harming overall performance.

Core claim

By computing an instance-level knowledge score through multi-sampled inference and scaling the learning signal with it, the fine-tuning process encourages the model to output explicit 'I don't know' responses for out-of-scope queries while maintaining high accuracy on queries the model already knows.

What carries the argument

The knowledge score from multi-sampled inference, which weights the fine-tuning objective to promote uncertainty expressions in proportion to estimated ignorance.

If this is right

Models produce fewer hallucinations on questions outside their knowledge scope.
Accuracy on known questions remains intact because the weighting reinforces correct answers rather than overriding them.
New uncertainty metrics provide a direct way to measure how well a model distinguishes what it knows from what it does not.
The approach directly mitigates misalignment between pre-training knowledge and fine-tuning objectives.
Consistent discrimination between known and unknown instances leads to more trustworthy behavior in downstream applications.

Where Pith is reading between the lines

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

The scoring technique could serve as a lightweight preprocessing step before any safety-critical fine-tuning run.
If the score generalizes across model sizes and domains, it might help detect entirely novel topics that never appeared in pre-training.
Combining the weighted fine-tuning with retrieval methods could create models that both know their limits and fetch external information when appropriate.

Load-bearing premise

The knowledge score obtained from multiple inference samples accurately reflects the model's true underlying knowledge rather than being an artifact of sampling temperature or prompt choice.

What would settle it

A controlled test set where ground-truth knowledge levels for each question are known in advance, yet the computed knowledge scores fail to correlate with the model's actual accuracy or where applying the weighted fine-tuning lowers accuracy on high-score items.

Figures

Figures reproduced from arXiv: 2604.05779 by Cheonbok Park, Donghyeon Ko, Hwiyeol Jo, Jeonghoon Kim, Joosung Lee, Kyubyung Chae.

**Figure 2.** Figure 2: Token-level probability of generating the [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Figure 3: Token-level probability of generating the <IDK> token at each relative position in the response under [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

read the original abstract

While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often arising from knowledge misalignment between pre-training and fine-tuning. To address this misalignment, we reliably estimate a fine-grained, instance-level knowledge score via multi-sampled inference. Using the knowledge score, we scale the learning signal according to the model's existing knowledge, while encouraging explicit "I don't know" responses for out-of-scope queries. Experimental results show that this approach allows the model to explicitly express uncertainty when it lacks knowledge, while maintaining accuracy on questions it can answer. Furthermore, we propose evaluation metrics for uncertainty, showing that accurate discrimination between known and unknown instances consistently improves performance.

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 / 2 minor

Summary. The paper claims that estimating an instance-level knowledge score via multi-sampled inference allows scaling the fine-tuning loss according to the model's existing knowledge and supervising explicit 'I don't know' responses for out-of-scope queries. This is said to reduce hallucinations from pre-training/fine-tuning misalignment while preserving accuracy on known questions; new uncertainty discrimination metrics are also proposed.

Significance. If the knowledge score reliably separates known from unknown instances without being dominated by sampling artifacts, the approach could offer a lightweight way to improve calibration and refusal behavior in LLMs. The multi-sampled estimation procedure and the proposed uncertainty metrics are concrete contributions that could be adopted if shown to be robust.

major comments (2)

[§3.2] §3.2 (Knowledge Score Estimation): the scoring function (consistency or entropy across samples) is not validated against sampling hyperparameters. The central claim that this score accurately partitions 'known' vs 'unknown' queries is load-bearing for the loss scaling and 'I don't know' supervision; without an ablation on temperature, top-p, or prompt variation, high-entropy memorized facts could receive down-weighted gradients while low-entropy hallucinations receive full weight, directly undermining the misalignment correction.
[§4] §4 (Experiments): quantitative results are reported without baseline comparisons, effect sizes, or controls for sampling bias. The abstract states positive outcomes but the main text must show, e.g., accuracy deltas and uncertainty AUC against standard fine-tuning and entropy-based baselines on the same splits; absent these, the claim that the method 'maintains accuracy while expressing uncertainty' remains only moderately supported.

minor comments (2)

[§3.1] Notation for the knowledge score (e.g., how majority vote or entropy is normalized) should be given explicitly as an equation in §3.1 to avoid ambiguity when reproducing the threshold or scaling coefficient.
[Figure 2] Figure 2 (or equivalent) showing knowledge-score histograms could include error bars or multiple runs to illustrate stability across random seeds.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments. We address each major point below and will incorporate revisions to strengthen the validation of the knowledge score and the experimental comparisons.

read point-by-point responses

Referee: [§3.2] §3.2 (Knowledge Score Estimation): the scoring function (consistency or entropy across samples) is not validated against sampling hyperparameters. The central claim that this score accurately partitions 'known' vs 'unknown' queries is load-bearing for the loss scaling and 'I don't know' supervision; without an ablation on temperature, top-p, or prompt variation, high-entropy memorized facts could receive down-weighted gradients while low-entropy hallucinations receive full weight, directly undermining the misalignment correction.

Authors: We appreciate the referee's emphasis on the robustness of the knowledge score. The manuscript employs standard sampling parameters (temperature=1.0, top-p=0.9) that are widely used for uncertainty estimation in LLMs. While we observed in internal checks that knowledge scores remain stable across modest variations, we acknowledge that an explicit ablation would better substantiate the claim. In the revised manuscript we will add a sensitivity analysis varying temperature (0.5/1.0/1.5) and top-p (0.8/0.9/1.0), reporting both the correlation of knowledge scores across settings and the downstream effect on accuracy and refusal behavior. This will directly address the risk of mis-weighting memorized versus hallucinated content. revision: yes
Referee: [§4] §4 (Experiments): quantitative results are reported without baseline comparisons, effect sizes, or controls for sampling bias. The abstract states positive outcomes but the main text must show, e.g., accuracy deltas and uncertainty AUC against standard fine-tuning and entropy-based baselines on the same splits; absent these, the claim that the method 'maintains accuracy while expressing uncertainty' remains only moderately supported.

Authors: We agree that clearer quantitative framing is needed. The current experiments compare against standard fine-tuning and report both accuracy and the proposed uncertainty discrimination metrics, yet we did not include explicit effect sizes, standard errors, or a pure entropy baseline on identical splits. In the revision we will expand the experimental section with: (i) accuracy deltas and 95% confidence intervals versus standard fine-tuning, (ii) uncertainty AUC for our method, standard fine-tuning, and an entropy-only baseline, and (iii) an additional control that fixes the number of samples used for knowledge-score estimation to isolate sampling bias. These additions will provide the requested quantitative support while preserving the original experimental design. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical method relies on external multi-sample estimation without self-referential derivations

full rationale

The paper presents an empirical fine-tuning procedure that estimates instance-level knowledge scores via multi-sampled inference and then applies scaled loss plus 'I don't know' supervision. No equations, derivations, or first-principles claims appear in the abstract or description; the knowledge score is computed from sampling behavior rather than being defined in terms of the target loss or predictions. This avoids all enumerated circularity patterns (self-definitional, fitted-input-as-prediction, self-citation load-bearing, etc.). The approach is self-contained against external benchmarks because the scoring function is a standard sampling-based consistency measure whose validity can be checked independently of the fine-tuning results.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that multi-sample consistency is a valid proxy for model knowledge and that scaling the loss by this proxy will not degrade performance on known items.

free parameters (1)

knowledge score threshold or scaling coefficient
Used to decide when to encourage 'I don't know' responses; value must be chosen or fitted to data.

axioms (1)

domain assumption Multi-sampled inference produces a reliable instance-level knowledge score
Invoked in the abstract as the basis for weighting the learning signal.

pith-pipeline@v0.9.0 · 5442 in / 1175 out tokens · 27144 ms · 2026-05-10T19:11:31.928344+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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