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arxiv: 2604.05757 · v1 · submitted 2026-04-07 · 💻 cs.CL

Identifying Influential N-grams in Confidence Calibration via Regression Analysis

Shintaro Ozaki , Wataru Hashimoto , Hidetaka Kamigaito , Katsuhiko Hayashi , Taro Watanabe This is my paper

Pith reviewed 2026-05-10 19:21 UTC · model grok-4.3

classification 💻 cs.CL
keywords confidence calibrationn-gramslarge language modelsregression analysisoverconfidencereasoningQA benchmarkslinguistic expressions
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The pith

Large language models can be made less overconfident by suppressing particular linguistic expressions in their reasoning chains, with no loss in task performance.

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

Large language models often express uncertainty in their reasoning but still give overconfident final answers. The authors use regression to find which n-grams in the reasoning text most strongly predict the model's confidence level. They discover that certain expressions, some used to boost reasoning, actually increase overconfidence. Tests confirm that these expressions causally affect confidence. Suppressing them allows calibration of confidence without reducing performance on question answering tasks.

Core claim

The central discovery is that specific n-grams in LLM reasoning chains are responsible for overconfidence, identified via regression where n-gram indicators predict confidence scores. These n-grams include cue phrases from scaling methods. By suppressing them, confidence can be calibrated while maintaining performance across models and benchmarks, as verified by causality tests.

What carries the argument

Linear regression analysis with n-gram features as predictors and LLM confidence as the target variable, used to extract influential linguistic expressions.

Load-bearing premise

That the n-grams identified by regression coefficients causally influence confidence rather than merely correlating with it, and that their suppression does not impair reasoning on unseen data.

What would settle it

Observing no reduction in overconfidence or a decrease in task accuracy when the identified n-grams are suppressed in new model-task combinations.

Figures

Figures reproduced from arXiv: 2604.05757 by Hidetaka Kamigaito, Katsuhiko Hayashi, Shintaro Ozaki, Taro Watanabe, Wataru Hashimoto.

Figure 1
Figure 1. Figure 1: Overview of our work that segments the reasoning process into [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Our calibration approach, N-gram Suppression and Injection. While n-grams may indicate overconfidence, the cause of overconfidence is not necessarily limited to n-grams, i.e., factors other than n-grams may con￾tribute to overconfidence. In order to investigate the issue, we verify whether we can manipulate confidence by instructing the model to suppress or actively use specific linguis￾tic information obt… view at source ↗
Figure 3
Figure 3. Figure 3: Results of the calibration plot. Ideally, the curve matches the diagonal line, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Changes in n-gram frequencies. These results suggest that the distribution of expressions used by the model shifts during the regeneration process, biasing toward negative or positive expressions. Finally, we emphasize that the main contribution of this work is the causal analysis of linguistic expressions associated with model confidence, while a systematic comparison with existing calibration methods is … view at source ↗
Figure 5
Figure 5. Figure 5: Results of extracting n-grams common to the models. 5.3 Influential N-grams on Accuracy and Confidence In Tables 2 and 3, blue indicates n-grams that Lasso regression identifies as contributing to underconfidence and logistic regression identifies as contributing to positive accuracy. Conversely, red indicates n-grams that contribute to overconfidence and negative accuracy. Our result reveals that while n-… view at source ↗
Figure 6
Figure 6. Figure 6: Calibration curve obtained using the generation-based method. [PITH_FULL_IMAGE:figures/full_fig_p022_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Correlation between TF-IDF-based and count-based methods. [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
read the original abstract

While large language models (LLMs) improve performance by explicit reasoning, their responses are often overconfident, even though they include linguistic expressions demonstrating uncertainty. In this work, we identify what linguistic expressions are related to confidence by applying the regression method. Specifically, we predict confidence of those linguistic expressions in the reasoning parts of LLMs as the dependent variables and analyze the relationship between a specific $n$-gram and confidence. Across multiple models and QA benchmarks, we show that LLMs remain overconfident when reasoning is involved and attribute this behavior to specific linguistic information. Interestingly, several of the extracted expressions coincide with cue phrases intentionally inserted on test-time scaling to improve reasoning performance. Through our test on causality and verification that the extracted linguistic information truly affects confidence, we reveal that confidence calibration is possible by simply suppressing those overconfident expressions without drops in 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

3 major / 2 minor

Summary. The paper claims that regression analysis on n-grams appearing in LLM reasoning chains can identify linguistic expressions causally linked to verbalized confidence. Across multiple models and QA benchmarks, it reports that LLMs remain overconfident during reasoning, attributes this to specific n-grams (some overlapping with test-time scaling cue phrases), and concludes that suppressing the overconfident expressions yields better calibration without drops in performance, based on causality tests and verification experiments.

Significance. If the regression truly isolates causal n-gram effects and the suppression intervention generalizes without side effects on reasoning validity, the work would provide an interpretable, low-cost method for post-hoc confidence calibration in LLMs. It would also strengthen connections between linguistic cue analysis and existing test-time scaling techniques.

major comments (3)
  1. [Abstract and Methods] Abstract and Methods: the regression setup predicts confidence from n-gram presence/count but provides no specification of the model (e.g., linear vs. logistic, regularization, handling of multicollinearity among n-grams), no multiple-testing correction across thousands of candidate n-grams, and no controls for obvious confounders such as answer correctness, reasoning length, or task type.
  2. [Causality and Verification experiments] Causality and Verification experiments: the suppression intervention is described as isolating the effect of the extracted expressions, yet no evidence is given that it holds response length, chain-of-thought coherence, or alternative phrasings constant; without such controls the claimed causal influence on confidence (independent of the surrounding reasoning) remains unestablished.
  3. [Experiments and Results] Generalization claims: the portability of the identified n-grams to unseen models and tasks rests on the assumption that they are not dataset- or model-specific artifacts, but the paper reports no cross-model ablation that removes the n-grams while preserving answer correctness and reasoning validity.
minor comments (2)
  1. [Abstract] Notation: the abstract uses '$n$-gram' in LaTeX but the full text should consistently define the range of n (unigram, bigram, etc.) and the exact feature representation (binary presence, count, TF-IDF) in the regression.
  2. [Results] The paper should include a table listing the top extracted n-grams with their regression coefficients and statistical significance for each model/benchmark pair.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful and constructive comments on our manuscript. We address each major comment point by point below and indicate the revisions we will undertake to improve clarity, rigor, and support for our claims.

read point-by-point responses

  1. Referee: [Abstract and Methods] Abstract and Methods: the regression setup predicts confidence from n-gram presence/count but provides no specification of the model (e.g., linear vs. logistic, regularization, handling of multicollinearity among n-grams), no multiple-testing correction across thousands of candidate n-grams, and no controls for obvious confounders such as answer correctness, reasoning length, or task type.

    Authors: We agree that the original manuscript omitted key details on the regression setup. In the revised version, we will fully specify the regression model (type, regularization, and multicollinearity handling), describe the multiple-testing correction applied across n-gram candidates, and detail the controls included for confounders such as answer correctness, reasoning length, and task type. These clarifications will be added to the Methods section. revision: yes

  2. Referee: [Causality and Verification experiments] Causality and Verification experiments: the suppression intervention is described as isolating the effect of the extracted expressions, yet no evidence is given that it holds response length, chain-of-thought coherence, or alternative phrasings constant; without such controls the claimed causal influence on confidence (independent of the surrounding reasoning) remains unestablished.

    Authors: The referee correctly identifies that stronger controls are needed to support the causal claims. We will revise the causality and verification section to include explicit evidence that response length is preserved via length-matched replacements, that chain-of-thought coherence is maintained (verified through both automated metrics and manual review), and that alternative phrasings were tested to isolate the n-gram effects. These additions will better substantiate the independent influence on confidence. revision: yes

  3. Referee: [Experiments and Results] Generalization claims: the portability of the identified n-grams to unseen models and tasks rests on the assumption that they are not dataset- or model-specific artifacts, but the paper reports no cross-model ablation that removes the n-grams while preserving answer correctness and reasoning validity.

    Authors: We acknowledge the lack of a dedicated cross-model ablation in the current experiments. In the revised manuscript, we will add ablation studies applying n-gram suppression to additional models and tasks, reporting that answer correctness and reasoning validity are preserved via accuracy metrics and coherence assessments. This will provide direct evidence supporting the generalization claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical regression and intervention chain is self-contained.

full rationale

The paper fits a linear regression with n-gram presence/count features as inputs and LLM verbalized confidence as the target variable, extracts high-coefficient n-grams, then runs separate causality tests and suppression interventions on held-out model outputs. These steps operate on external benchmark data and model generations; the final claim that suppression improves calibration metrics without accuracy loss is an observed experimental outcome rather than a definitional identity or a prediction forced by the same fitted parameters. No load-bearing self-citations, imported uniqueness theorems, or ansatzes are invoked to close the derivation. The analysis therefore remains non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides insufficient detail to enumerate specific free parameters, axioms, or invented entities; the work is framed as empirical regression on linguistic features extracted from model outputs.

pith-pipeline@v0.9.0 · 5457 in / 1138 out tokens · 36271 ms · 2026-05-10T19:21:07.170302+00:00 · methodology

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