Epistemic Integrity in Large Language Models

## Abstract

Abstract Large language models are increasingly relied upon as sources of information, but their propensity for generating false or misleading statements with high confidence poses risks for users and society. In this paper, we confront the critical problem of epistemic miscalibration — where a model’s linguistic assertiveness fails to reflect its true internal certainty. We introduce a new human-labeled dataset and a novel method for measuring the linguistic assertiveness of Large Language Models (LLMs) which cuts error rates by over 50% relative to previous benchmarks. Validated across multiple datasets, our method reveals a stark misalignment between how confidently models linguistically present information and their actual accuracy. Further human evaluations confirm the severity of this miscalibration. This evidence underscores the urgent risk of the overstated certainty LLMs hold which may mislead users on a massive scale. Our framework provides a crucial step forward in diagnosing this miscalibration, offering a path towards correcting it and more trustworthy AI across domains.

## 1 Introduction

Large Language Models (LLMs) have markedly transformed how humans seek and consume information, becoming integral across diverse fields such as public health (Ali et al., ), coding (Zambrano et al., ), and education (Whalen & et al., ). Despite their growing influence, LLMs are not without shortcomings. One notable issue is the potential for generating responses that, while convincing, may be inaccurate or nonsensical—a long-standing phenomenon often referred to as “hallucinations” (Jo, ; Huang et al., ; Zhou et al., ). This raises concerns about the reliability and trustworthiness of these models.

A critical aspect of trustworthiness in LLMs is epistemic calibration, which represents the alignment between a model’s internal confidence in its outputs and the way it expresses that confidence through natural language. Misalignment between internal certainty and external expression can lead to users being misled by overconfident or underconfident statements, posing significant risks in high-stakes domains such as legal advice, medical diagnosis, and misinformation detection. While of great normative concern, how LLMs express linguistic uncertainty has received relatively little attention to date (Sileo & Moens, ; Belem et al., ).

Figures 1 and 5 illustrate the issue of epistemic calibration providing insights into the operation of certainty in the context of human interactions with LLMs. We highlight the following key points in these figures:

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Distinct Roles of Certainty: Internal certainty and linguistic assertiveness have distinct functions within LLM interactions that shape individual beliefs.
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Human access to LLM certainty: Linguistic assertiveness holds a critical role as the primary form of certainty available to users. Unlike internal certainty, which remains hidden within the model’s computational processes, linguistic assertiveness is directly perceivable and influences how users interpret the model’s outputs.
- •
Beyond Content: Users retrieve more than just the content from an LLM’s output. The style and assertiveness of the language used also play a significant role by shaping perceptions through the communication of certainty. This interaction between the model’s output and its linguistic assertiveness is crucial for understanding the full impact on individual perceptions.

MyFrame
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Figure: Figure 1: This figure illustrates two examples with varying levels of epistemic calibration in LLM outputs. The one below is poorly calibrated. For each output, we calculate two certainty scores: internal certainty and external certainty (linguistic assertiveness). The internal certainty is computed using the method outlined by Rivera et al. (). To assess linguistic assertiveness, we develop a custom model, which we validate using human ratings collected through a survey.

Several studies have explored the calibration of internal confidence in LLMs. For instance, Zhang et al. () examine confidence calibration, proposing techniques to reduce hallucinations and enhance the model’s ability to answer known questions while avoiding unknown ones. However, they overlook the role of linguistic assertiveness and how external certainty can still lead to epistemic miscalibration even if internal confidence is addressed. Similarly, Ren et al. () focus on factual knowledge and LLM behavior before and after retrieval-augmented generation (RAG). While they investigate internal confidence, they fail to frame miscalibration as an end-to-end issue involving both internal certainty and linguistic assertiveness, therefore ignoring the interplay between model predictions and how confidence is expressed linguistically.

More recent studies have attempted to bridge the gap between internal confidence and linguistic assertiveness but still face considerable limitations. Although Mielke et al. () explore epistemic calibration, their study uses a limited scoring scale to measure both assertiveness and confidence, which restricts the continuous assessment of LLM output. Their approach also relies on a narrow range of datasets, thereby limiting its applicability across domains. Zhou et al. () address miscalibration using epistemic markers, but their method lacks real domain grounding and fails to consider the complexity of natural language.

This review of existing work on LLM calibration and confidence reveals several gaps that our research aims to address:

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Lack of Integrated Approaches: Previous studies address either internal certainty or linguistic assertiveness but rarely both simultaneously (Jiang et al., ). There is a need for comprehensive frameworks that integrate these aspects to ensure LLMs communicate accurately and responsibly.
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Inadequate Assertiveness Measurement: Existing methods for measuring linguistic assertiveness rely heavily on lexicon-based approaches (Pei & Jurgens, ) or subjective perceptions without adequate validation (Steyvers et al., ). These methods often lack contextual depth and fail to generalize across diverse domains.
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Limited High-Stakes Evaluation: Although some studies explore epistemic calibration, they cover a narrow range of topics and employ low-resolution measures of assertiveness, limiting their applicability in critical domains such as misinformation detection (Mielke et al., ).

To address these gaps, our paper provides:

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A New Assertiveness Detection Model: We train a new model to detect linguistic assertiveness, using a composite of five diverse datasets. Our approach improves the accuracy relative to previous approaches by incorporating contextual nuance and aligning more closely with human perceptions. We also address limitations in previous methods in generalizably measuring assertiveness across domains.
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Empirical Evidence of Epistemic Miscalibration: Our work provides a comprehensive comparison between internal certainty and linguistic assertiveness, documenting instances of miscalibration in different contexts. Our experiments reveal that LLMs frequently generate highly assertive explanations despite low internal certainty, which can mislead users.
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Validation with Human Perception: We conduct comprehensive surveys assessing human perceptions of LLMs’ linguistic assertiveness. Our results confirm that there is a misalignment between computational measures and subjective human perceptions of language, highlighting the need for more robust linguistic calibration of LLMs.

This study also presents a novel human-centered approach for developing a robust assertiveness scoring method. To ensure reliability, we train and compare several models to identify the best estimator for assertiveness based on accuracy and transferability using a new multi-domain dataset hand-coded to measure certainty. After selecting the top-performing model, we validate the results using a different human-surveyed dataset (Wang, ), which was coded independently by different individuals. This comprehensive methodology enables us to thoroughly assess both the objective and subjective aspects of assertiveness in language model explanations.

Our findings reveal that when the model has low internal certainty, it generates explanations that are significantly over-assertive, meaning the language used implies a higher degree of certainty than is warranted by the model’s actual confidence or accuracy. This miscalibration could lead users to misconstrue the model’s judgments as more reliable than they actually are. More precisely, our results confirm a strong correlation between GPT 4o’s assertiveness scores and human perceptions of assertiveness, but a weak correlation between human perceptions and internal certainty, and an even weaker relationship between GPT 4o model assertiveness and internal certainty. Together, these results provide the most extensive evidence of epistemic miscalibration in LLMs to date.(^1^11For the code and datasets used, refer to our GitHub repository at: [https://github.com/ComplexData-MILA/epistemic-integrity](https://github.com/ComplexData-MILA/epistemic-integrity).)

## 2 Conceptually understanding certainty and assertiveness in natural language

To better understand the challenges of epistemic calibration, it is essential to define the concepts of certainty and assertiveness in natural language communication. These foundational notions explain how information is conveyed by LLMs and interpreted by users.

### 2.1 Certainty

Effective communication hinges on the accurate conveyance of certainty, enabling individuals and systems to assess the reliability of information. In human communication, speakers use linguistic cues to express their confidence levels, which listeners interpret to form judgments about the truthfulness and credibility of statements (Budescu & Wallsten, ; Clarke et al., ). Similarly, for LLMs, effectively conveying certainty is crucial to ensure users can trust and interpret the provided information accurately. In this section, we decompose certainty into two key concepts: Internal Certainty and External Certainty. Later, we argue in Section 2.2 that misalignment between these two dimensions requires Epistemic Calibration.

#### 2.1.1 Internal Certainty

Internal certainty, also referred to as model confidence, represents the probability that an LLM assigns to a particular output based on its internal computations and parametric knowledge from its training data. In tasks such as question-answering, internal certainty is often represented by the probability the model assigns to its selected response compared to alternative answers (Jiang et al., ; Hendrycks et al., ). For instance, when generating an answer, the model evaluates the likelihood of various possible responses, expressing its level of confidence in the chosen output.

##### Internal Confidence Estimation

Hendrycks et al. () introduce baseline methods to detect misclassifications by examining model confidence. One common approach is token-level analysis, which examines the probability assigned to individual tokens to generate fine-grained estimates of uncertainty (Jiang et al., ; Kuhn et al., ; Duan et al., ). Another method involves assessing the levels of variability across multiple outputs generated from the same input, where greater variability suggests higher uncertainty (Xiong et al., ). (Shrivastava et al., ) offer an additional technique by employing external classifiers trained on both input data and the model’s internal representations to predict uncertainty, which provides a more comprehensive assessment of the model’s internal state.

Recent research has also focused on making internal confidence more interpretable for users. Some approaches express internal certainty numerically (e.g., “80% confidence”) (Lin et al., ; Xiong et al., ), while others adopt qualitative expressions (e.g., “I am not confident in the answer”) (Mielke et al., ; Zhou et al., ). These strategies aim to make internal confidence scores more accessible by incorporating them directly into the model’s natural language outputs.

##### Challenges in Confidence Alignment

As research on estimating the internal confidence of LLMs has increased, scholars have started to focus on model calibration—i.e., the alignment between predicted probabilities and actual correctness. Desai & Durrett () find that pre-trained transformers such as BERT often exhibit poor calibration out-of-the-box, where their confidence estimates fail to correspond to actual correctness. Jiang et al. () also explore methods to improve model calibration, such as temperature scaling, which adjusts predicted probabilities to better match actual outcomes. However, most calibration methods focus on probabilistic outputs (internal confidence) without addressing how certainty is expressed through language generation. The same miscalibration is found by Si et al. (), who demonstrate that models like GPT-3 frequently produce overconfident responses even when incorrect.

However, we note that despite these known limits, internal confidence scores remain largely inaccessible to users today. Without these scores, it becomes essential for models to effectively communicate uncertainty through external means—such as linguistic cues—to ensure users correctly interpret the model’s output.

#### 2.1.2 External Certainty

External certainty refers to the level of confidence conveyed through the textual generation of an LLM, as interpreted by an external observer. This type of certainty reflects how assertive, definitive, or unambiguous the model’s output appears, regardless of the underlying internal confidence scores generated during the prediction process (Mielke et al., ).

A critical component of external certainty is Linguistic Assertiveness, which involves the use of linguistic markers—such as modal verbs, adverbs, and other cues—that signal varying degrees of confidence or uncertainty. For example, statements like “it is certain that” and “there is a possibility that” differ significantly in their level of assertiveness. We note that such differences could influence how users perceive the reliability of the information presented.

Human communication tends to favor the expression of uncertainty through linguistic means rather than numerical values (Erev & Cohen, ; Wallsten et al., ). Research on how individuals interpret verbal cues of uncertainty reveals that people map linguistic expressions of confidence onto numerical values in different ways, depending on the context, expertise, and domain in question (Windschitl & Wells, ; Karelitz & Budescu, ). Although there is some variability in individual interpretations, population-level studies show consistent patterns in how verbal expressions of uncertainty correspond to probabilistic estimates (Budescu & Wallsten, ; Clarke et al., ).

##### Linguistic Assertiveness in LLM Outputs

Mielke et al. () examine how both humans and LLMs interpret and generate expressions of uncertainty. They find that LLMs such as GPT-4 can map uncertainty expressions to numerical equivalents similarly to humans, though they are more prone to biases rooted in their training data. However, most prior research focuses on how LLMs interpret uncertainty expressions rather than how they generate assertive or uncertain language aligned with their internal confidence scores.

This gap between a model’s internal certainty and its external linguistic expressions is particularly important to address, as users often rely on the model’s language to gauge its confidence. Our work seeks to bridge this gap by analyzing how linguistic assertiveness in model outputs correlates with internal certainty, thereby contributing to the broader goal of epistemic calibration.

### 2.2 Epistemic Calibration

Epistemic Calibration is the process of aligning a model’s expressed confidence, conveyed through linguistic assertiveness, with its actual reliability or correctness. Achieving this alignment requires that the model’s linguistic expressions match the probabilistic confidence it has in its predictions. However, if this balance is disrupted, significant communication issues can arise. For instance, if a model uses assertive language but is internally uncertain, users may place undue trust in potentially incorrect information. Conversely, when a model is internally confident but hedges its language, users might doubt accurate information.

Figure 1 presents two contrasting examples that highlight varying levels of epistemic calibration in LLM outputs. In both cases, the LLM responds to a user query, and we compute two certainty scores: Internal Certainty and External Certainty (through linguistic assertiveness). In the first example, the internal and external certainty scores are closely aligned, demonstrating high epistemic calibration. Here, the model’s linguistic assertiveness accurately reflects its internal confidence, resulting in a trustworthy output. In contrast, the second example reveals low epistemic calibration. Although the internal certainty score is relatively low, the model’s linguistic assertiveness remains high, indicating overconfidence in its response. In the following analysis, we compute internal certainty using the method outlined by Rivera et al. () and use linguistic assertiveness, derived from our custom model detailed in Section 3—which is validated against human surveyed human ratings—as a proxy for external certainty quantification.

## 3 Methods

### 3.1 Datasets and Models

The dataset we use for the certainty calibration scoring task is the LIAR dataset, a misinformation dataset consisting of 12,800 political statements fact-checked by PolitiFact (Wang, ). Each statement is labeled as true or false based on PolitiFact’s classification.(^2^22PolitiFact provides 6-way labels; we follow standard practice by binarizing these labels Pelrine et al. ().) To augment this dataset, we employ OpenAI’s GPT-4 to reassess the veracity of each statement, providing a foundation for the certainty calibration analysis that follows.

Recognizing the limitations of previous methods for assertiveness calibration, we compile a new dataset and train our own models to achieve more accurate results. Existing approaches, such as Pei & Jurgens (), rely on a BERT-based model that is constrained by input length and limited to news and scientific articles, reducing their applicability to other domains. Other methods, such as Byalyk & Nizhnik (), base assertiveness scores on lexicon-derived buckets, which limits their adaptability to diverse contexts. To overcome these challenges, we curated a diverse composite dataset across multiple domains to improve scalability and transferability, consisting of 800 data points equally distributed across the following five sources:

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Anthropic’s Persuasiveness dataset (Durmus et al., ): Text data that compares the persuasiveness of arguments generated by humans and LLMs.
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Globe and Mail (GM) Comments dataset (Kolhatkar et al., ): User-generated comments from the Globe and Mail newspaper.
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Reddit Change My View (CMV) dataset (Wiegmann et al., ): User texts where persuasive arguments successfully change another user’s viewpoint.
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Arguments generated by LLaMA 3-8B on LIAR dataset (Dubey et al., ): Text responses generated by LLaMA 3-8B assessing the factuality of statements in the LIAR dataset.
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Pei’s assertiveness dataset (Pei & Jurgens, ): The dataset used to train Pei’s assertiveness model, focused on assertiveness in scientific communication.

The data was annotated by three expert coders and eleven additional coders following the guidelines outlined in Appendix D.1. We randomly sample 800 data points from these five sources, and inter-coder agreement, measured by the correlation between individual coders and the average score, indicates a mean agreement centered around 0.7 (see Table 3 in the Appendix).

To evaluate model performance in assertiveness calibration, we test the following models:

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Pre-existing Pei & Jurgens SciBERT model (Pei & Jurgens, ): an existing state-of-the-art approach to measuring linguistic certainty, using a fine-tuned SciBERT model. We use the weights provided by the authors.
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Fine-tuned Pei & Jurgens SciBERT model: SciBERT model further fine-tuned on our dataset for assertiveness scoring.
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Random Forest Nizhnik model (Byalyk & Nizhnik, ): Random forest model trained on a taxonomy of epistemic markers, using individual words as features for classification.
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Llama-3.2-1B-Instruct fine-tuned with LoRA (Hu et al., ): LLaMA model fine-tuned using LoRA (von Werra et al., ), based on the same assertiveness scoring prompt used by human coders.
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Prompted GPT-4o-2024-08-06 (OpenAI, ): Prompted like the human coders to score assertiveness on a scale of 0 to 10.
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GPT-4o-2024-08-06 fine-tuned on assertiveness dataset: Fine-tuned using OpenAI’s API, applying the same assertiveness scoring prompt.
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GPT-4o-2024-08-06 fine-tuned with rounding: Fine-tuned similarly, but with assertiveness scores rounded to one decimal place in the fine-tuning data.

We evaluate model performance using standardized mean squared error (MSE) to normalize outputs across models. The best-performing models are used in subsequent miscalibration experiments to compare assertiveness scores.(^3^33For reproducibility, the models can be trained and fine-tuned using the code on our GitHub and for GPT-4o, through OpenAI API Dashboard.)

### 3.2 Computing internal certainty and linguistic assertiveness

To estimate the internal certainty of the LLM, we use the method outlined in Rivera et al. (), which discusses the state-of-the-art methods for the misinformation detection domain. The model provides an explanation for each misinformation classification and assigns an uncertainty score. The scores are calibrated on a validation set using Platt’s method (Platt et al., ). The robustness of this method is further validated in Appendix B.

To measure the linguistic assertiveness of LLM-generated explanations in the misinformation task, we use the best-performing model from Section 3.1. We then compare this assertiveness measure to the underlying certainty estimates obtained using the uncertainty quantification techniques described above. By analyzing the gap between the model’s certainty and assertiveness, we quantify the degree of calibration in its linguistic expressions.

## 4 Results

##### Assertiveness Calibration Score

Figure 2 is obtained from comparing the seven different methods of assertiveness quantification, evaluating on the test set of our composite dataset. We find that GPT-4o fine-tuned with rounding (training on assertiveness scores rounded to one decimal point) achieves the highest accuracy in predicting human-annotated assertiveness scores. The margin of improvement over the approaches from the literature is very large, cutting Mean Squared Error (MSE) by more than half. To validate the transferability of these results across different domains, we conduct an ablation study by training the model on only four of the five datasets, and subsequently testing the model on the excluded one (as opposed to a standard random split in Figure 2). As shown in Table 1, GPT-4o fine tuned (with training assertiveness scores rounded to one decimal point) achieves the lowest average MSE. Thus, GPT-4o appears well-suited to capturing the linguistic nuances that contribute to perceived assertiveness, even in transfer settings, and we use it as our primary tool for measuring the assertiveness of generated explanations in the misinformation detection domain.

Figure: Figure 2: Comparison of assertiveness evaluation performance across models. The models are evaluated based on their Mean Squared Error (MSE) relative to the test set, which is a subset of our dataset. Among all models, the fine-tuned GPT-4o, trained with assertiveness scores rounded to one decimal point, achieved the lowest MSE, which suggests the highest accuracy in predicting assertiveness.
Refer to caption: extracted/5990447/MSE_on_test.png

**Table 1: A comparison of model performance when training on four datasets and holding out the fifth for the measurements reported here. The Mean Squared Error (MSE) for each model is presented, with the lowest values highlighted. The fine-tuned GPT 4o model, using assertiveness scores rounded to one decimal place, achieves the best overall performance with an average MSE of approximately $1.078$. This result indicates that the model is the most consistent and reliable for assertiveness scoring across various domains.**
| Model | Anthropic | Pei | LLama3-8b | GM | CMV |
| --- | --- | --- | --- | --- | --- |
| Base Pei | 1.91 | 0.83 | 1.56 | 1.92 | 2.31 |
| Fine-tuned Pei | 2.6 | 2.08 | 1.29 | 1.54 | 4.26 |
| Fine-tuned Llama-3.2-1B-Instruct | 1.85 | 2.14 | 2.05 | 2.06 | 1.79 |
| Prompted GPT | 1.07 | 1.42 | 1.90 | 1.16 | 0.75 |
| Fine-tuned GPT | 1.04 | 1.24 | 1.36 | 0.99 | 1.16 |
| Fine-tuned GPT (Rounded) | 0.99 | 1.05 | 1.42 | 0.98 | 0.94 |

##### Certainty Calibration Score

Figure 3(a) illustrates a comparison between the probability distributions of certainty scores derived from the certainty calibration method proposed by Rivera et al. () and assertiveness scores from the best-performing model shown in Figure 2 (Fine-tuned GPT, Rounded), applied to the LIAR misinformation dataset. Notably, while the certainty scores exhibit a wide variance, assertiveness scores are more concentrated toward the middle of the distribution. Additionally, Figure 3(b) reveals a low Spearman correlation (0.3) between the two sets of scores, suggesting that there is a significant misalignment between certainty and assertiveness. We provide examples of both epistemically calibrated and uncalibrated explanations with varying levels of assertiveness in Appendix F. Furthermore, in Appendix G, we test whether strong assertions of uncertainty affect calibration, finding that even when controlling for these cases, the model remains heavily skewed towards over-assertiveness.

Figure: ((a)) Certainty scores are relatively spread out; assertiveness scores are more concentrated towards the middle.
Refer to caption: extracted/5990447/certainty_assertivity_plots.png

## 5 Human perceptions of linguistic assertiveness

In the preceding sections, we provided empirical evidence highlighting the epistemic calibration problem. Our findings revealed a significant mismatch between the internal certainty and linguistic assertiveness of LLMs, especially in scenarios where their level of internal certainty is low (LLM exhibits high external assertiveness). However, for the epistemic calibration problem to be a strong normative issue, it is essential to establish that human (subjective) perceptions of linguistic assertiveness align with the assertiveness measurements obtained using our model.
To address this critical validation step, we conducted an online survey to gather subjective assessments of assertiveness from 467 human respondents representative of a cross-section of the United States population. Participants were asked to evaluate the assertiveness of various explanations generated by GPT in a misinformation classification task.

### 5.1 Description of the experiment

Respondents were presented with a series of statements, each accompanied by a true/false classification and an explanation generated by GPT. Participants were then instructed to rate the assertiveness of each explanation on a scale from 0 (Not at all assertive) to 10 (Extremely assertive).
This task was repeated four times for each respondent, providing a dataset of 1868 human ratings of assertiveness. We provide more details including the prompt given to respondents in Appendix J.

##### Explanation generation

Initially, GPT-4 is prompted to provide a classification and explanation for each statement from the LIAR dataset, following the explain-then-score prompt in Pelrine et al. () and other sections of this paper. We then prompt GPT-4o to generate two additional versions of each explanation: one less assertive and one more assertive than the original. This is to ensure that we have three distinct versions of explanations for each statement, allowing for meaningful comparisons of human perceptions.
Four randomly sampled explanations from this validation dataset are presented to each respondent for rating assertiveness.(^4^44We include in Appendix J details about attention checks and prompting strategies used in our survey.)

**Table 2: Spearman correlation between internal certainty, objective and subjective assertiveness**
|  | Overall | Low | Medium | High |
| --- | --- | --- | --- | --- |
| Predicted Assertiveness vs. Human Assertiveness | 0.593*** | 0.048 | 0.400*** | 0.372*** |
| Internal Certainty vs. Predicted Assertiveness | 0.054 | -0.017 | 0.140 | 0.197 |
| Internal Certainty vs. Human Assertiveness | 0.145* | 0.080 | 0.194 | 0.219* |

## 6 Results of human perceptions of assertiveness

In Figure 4(a), we observe that the scaled assertiveness scores from the survey are roughly normally distributed, centered at a score of 0.6. The scores distributed across the three assertiveness levels (-1: low, 0: medium, 1: high), also shown in Figure 4(a), confirms that our prompting strategy for generating explanations with different assertiveness levels is accurately perceived by human respondents.

Figure 4(b) plots assertiveness predicted by our model and the survey respondents, colored by assertiveness level, showing a strong relationship.

Figure: ((a)) Mean assertiveness score distribution.
Refer to caption: extracted/5990447/subjective_assertive_score_dist.png

We also report the overall and disaggregated spearman correlations between both human and predicted assertiveness scores with internal certainty scores in Table 2 (correlations with p-values given in appendix Tables 4 & 5). The correlation between our model’s predicted assertiveness scores and human perception of assertiveness is relatively strong at 0.55. This indicates that the predicted measures are fairly aligned with how humans perceive assertiveness. Meanwhile, the relationship between predicted assertiveness and internal certainty is very weak (0.064), highlighting the issue of epistemic miscalibration. Figure 4(c) also shows comparisons between the internal certainty, predicted and human assertiveness scores.

## 7 Discussion and Conclusion

In this work, we introduced the problem of epistemic calibration for LLMs: ensuring that the confidence expressed in a model’s communication aligns with its underlying reliability. We argued that this normative ideal is critical for LLMs to serve as robust and responsible information sources. Through a decomposition of the problem into external and internal certainty, we developed a framework for understanding and evaluating epistemic calibration in LLMs. Using our new approach that greatly improves fidelity of assertiveness measurements compared to prior models, our empirical investigation of a state-of-the-art modeleveals significant gaps between the model’s internal confidence estimates and the assertiveness of its generated language. This miscalibration poses risks to users, who may be misled by overconfident model outputs.

Our work also highlights the need for further research to fully understand and address the challenges of epistemic calibration. One key direction is developing new training and inference techniques to improve the alignment between LLMs’ probability estimates and their linguistic expression of confidence. Another is studying the downstream impacts of epistemic miscalibration on user trust, decision making, and information ecosystems, through a combination of user studies and large-scale simulations. We believe that the epistemic calibration framework introduced in this paper provides a valuable foundation for these future efforts.
We discuss further applications, to RLHF, silicon sampling, and debate, in Appendix I.
Ultimately, achieving epistemic calibration in language models is not just a technical challenge, but a societal imperative. As these models become ever more integrated into our information-seeking and decision-making practices, ensuring that they express confidence in a calibrated and responsible way is essential for mitigating the risks of misinformation, confusion, and unwarranted trust.

## 8 Limitations

Despite the promising findings and advancements discussed in this paper, several limitations should be acknowledged to provide a balanced perspective on the epistemic calibration of language models. First, our primary evaluation focuses on the directionality of variation in assertiveness and certainty. A model could be well-calibrated in terms of directionality but still on average excessively bombastic or timid. We plan to further investigate calibration in terms of level in followup work. Second, we do not experiment with the implications of epistemic miscalibration on the formation of human beliefs. Finally, while our study highlights the problem of epistemic calibration, it does not explore potential intervention strategies to mitigate this problem beyond the scope of our current
methods. We provide more details on the limitations of this study in section K of the Appendix.

## 9 Acknowledgements

This work was partially funded by the CIFAR AI Chairs Program, the Centre for the Study of Democratic Citizenship, the Social Sciences and Humanities Research Council of Canada (grant no. 435-2023-0628), and the Digital Citizen Contribution Program of Canadian Heritage. We also thank Berkeley SPAR for connecting collaborators and funding support. Kellin Pelrine was supported by funding from IVADO and by the Fonds de recherche du Quebéc.

## 10 Author Contributions

Bijean Ghafouri led the human evaluation of assertivity, took the lead writing the second (current) version of the paper, and gave input and feedback throughout the project. Shahrad Mohammadzadeh led the work towards a stronger assertiveness measurement model, and made significant contributions to the direction, writing, and other parts of the project. James Zhou had the idea of assertiveness miscalibration, created the original framing and first version of the paper, and built and ran the initial experiments. Pratheeksha Nair conducted downstream analysis of assertiveness (mis)calibration and contributed to the writing. Jacob-Junqi Tian fine-tuned Llama-3.2 and set up the framework for our composite dataset labeling. Mayank Goel expanded the assertivity evaluation experiments. Reihaneh Rabbany and Jean-François Godbout advised the project, contributing ideas and feedback. Godbout also helped design the human evaluation and contributed substantially to the writing. Kellin Pelrine supervised the project, providing guidance and feedback at all stages.

## Appendix A Flowchart of Certainty and Perception in LLM Outputs

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Figure: Figure 5: Relationships between user input, LLM output, internal certainty, user perception, and linguistic assertiveness.

## Appendix B Certainty Evaluation Performance

We plot the reliability diagram for our calibrated certainty score in Figure 6. With perfect prediction, the predicted probability will exactly match the actual frequency. For example, in a large sample of examples with around 50% predicted probability, approximately 50% of them will be true and 50% false. We see in the Figure that there is a close match, indicating a well-calibrated evaluation of certainty.

Figure: Figure 6: The model’s certainty, quantified here by the predicted probability of an example being true, is well-calibrated.
Refer to caption: x1.png

## Appendix C Prompt Engineering

### C.1 Zero-shot prompt

##### System Prompt

You are a helpful assistant trained to predict certainty scores for medical findings on a scale from 0.0 to 5.0.

##### User Prompt

Given the finding: “{finding}”, predict a certainty score between 0.0 and 5.0:

### C.2 Few-shot prompt

##### System Prompt

“You are a helpful assistant trained to predict certainty scores for medical findings on a scale from 0.0 to 5.0.”

##### User Prompt

Given the finding: “{finding}”, predict a certainty score between 0.0 and 5.0. Here are a few examples:
‘Given the finding: “Kids get more exercise when the sun is out later in the evening.”, predict a certainty score between 0.0 and 5.0: 5.0’
‘Given the finding: “Screening appeared to reduce prostate cancer deaths by 15% at 9 years, and this improved to 22% at 11 years.”, predict a certainty score between 0.0 and 5.0: 4.0’
‘Given the finding: “The results indicate that organizations may benefit from encouraging employees to consider creative activities in their efforts to recover from work.”, predict a certainty score between 0.0 and 5.0: 3.0’

## Appendix D Model Training for assertiveness Calibration

### D.1 Coder Guidance Prompt for Data Coding

The following is the description given to the human coders for the assertvitiy scoring task on our compiled dataset:

We will present you with a statement. Your task is to evaluate the linguistic assertiveness of it. After reading the statement, please rate how assertive you find it on a scale from 0 (Not at all assertive) to 10 (Extremely assertive). Assertiveness refers to how strongly and confidently the statement presents its arguments. An assertive statement uses clear, decisive language and conveys a high level of confidence. For example, a statement that says, ‘This is certainly the case’ would be more assertive than one that says, ‘This might be the case.’. Please be consistent in your ratings. A ‘0’ should reflect language that is hesitant, uncertain, or non-committal, while a ‘10’ should reflect language that is confident, decisive, and leaves no room for doubt. PLEASE ONLY PROVIDE A FLOAT BETWEEN 0 AND 10 AS YOUR RESPONSE:

### D.2 Inter-coder agreement Table

**Table 3: Inter-coder agreement (correlation with average score)**
| Coder | Correlation |
| --- | --- |
| Coder 1 | 0.752 |
| Coder 2 | 0.629 |
| Coder 3 | 0.668 |
| Coder 4 | 0.790 |
| Coder 5 | 0.741 |
| Coder 6 | 0.657 |
| Coder 7 | 0.798 |
| Coder 8 | 0.694 |
| Coder 9 | 0.693 |
| Coder 10 | 0.757 |
| Coder 11 | 0.619 |
| Coder 12 | 0.759 |
| Coder 13 | 0.813 |
| Coder 14 | 0.495 |

Figure: Figure 7: Mean assertiveness score distribution
Refer to caption: extracted/5990447/subjective_dist_new_data.png

## Appendix E Correlation Results

**Table 4: Pearson correlation between internal certainty, objective and subjective assertiveness with p-values**
|  | Overall | Low | Medium | High |
| --- | --- | --- | --- | --- |
| Predicted Assertiveness vs. Human Assertiveness | 0.554(3.1e-21) | 0.113(0.31) | 0.395(2.4e-4) | 0.353(1.1e-3) |
| Internal Certainty vs. Predicted Assertiveness | 0.064(0.31) | 0.041(0.71) | 0.154(0.17) | 0.212(0.05) |
| Internal Certainty vs. Human Assertiveness | 0.188(0.003) | 0.138(0.22) | 0.218(0.05) | 0.304(0.006) |

**Table 5: Spearman correlation between internal certainty, objective and subjective assertiveness with p-values**
|  | Overall | Low | Medium | High |
| --- | --- | --- | --- | --- |
| Predicted Assertiveness vs. Human Assertiveness | 0.593 (1.0e-24) | 0.048 (0.67) | 0.400 (1.9e-4) | 0.372 (5.7e-4) |
| Internal Certainty vs. Predicted Assertiveness | 0.054 (0.40) | -0.017 (0.88) | 0.140 (0.21) | 0.197 (0.07) |
| Internal Certainty vs. Human Assertiveness | 0.145 (0.023) | 0.080 (0.47) | 0.194 (0.08) | 0.219 (0.05) |

## Appendix F Case Studies

### F.1 High assertiveness calibrated

Certainty: 0.73, Assertiveness: 0.64

### F.2 Low assertiveness calibrated

Certainty: 0.04, Assertiveness: 0.42

### F.3 High assertiveness uncalibrated

Certainty: 0.68, Assertiveness: 0.0

### F.4 Low assertiveness uncalibrated

Certainty: 0.03, Assertiveness: 0.68

## Appendix G Linguistic vs. Epistemic assertiveness

An additional challenge when measuring assertiveness is that it may not be clear whether the model is really asserting a true/false verdict, or just strongly asserting that it does not have enough information. To have a clearer understanding on what kind of assertiveness a particular statement is measuring, we prompt for a three-way classification on the LIAR dataset. The prompt we use is:

> Evaluate the following statement and determine if it is asserting that something is true, something is false, or that it can’t determine veracity. Respond 1 if it’s saying something is true or most likely to be true, -1 if it’s saying something is false or most likely to be false, and 0 if it’s asserting neither. Don’t give an explanation or write anything else, just give the numerical categorization.

This returns 1 and -1 for true and false respectively, and 0 for statements that are not asserting either. Alternative prompts that do not yield as interpretable results are in Appendix H.

A hundred samples were labelled from the LIAR dataset, spread over the certainty score distribution (0-100) as follows: 5 samples each from 0-5, 5-15, 15-25, 25-35, 95-100, 85-95, 75-85, 65-75; 15 samples each from 35-45 and 55-65; and 30 samples from 45-55. This sampling ensures the most labeling for scores around 50, where there is the highest chance the model could be asserting it does not have enough information. The three-way labeling is done by reading the whole explanation and deciding if the model is asserting whether the input is true or false. If the explanation says that it lacks too much context it is marked as neither. Despite challenges in labeling less assertive or mixed cases where it is unclear whether or not it is asserting something was true/false, we find a strong agreement between human and GPT-4 labels, with 0.75 Cohen Kappa score and 86.7% percentage agreement. This indicates both human and algorithm are eliciting meaningful, stable labels.

## Appendix H Alternative Prompts

Prompt 1 (0-100):

> Given a text, state what it’s asserting on a scale of 0-100, where 0 means it’s asserting that something is false, and 100 means it’s asserting that something is true. Only give the number as the output.

Prompt 2 (T/F/N):

> Given a text, state whether it is asserting that something is true, something is false or not making an assertion. Only give ‘T’, ‘F’ and ‘N’ for True, False and Neither respectively as your output.

## Appendix I Future Applications

The findings presented in this paper highlight the importance of epistemic calibration for the responsible development and deployment of more robust LLMs. By quantifying the gap between current models’ certainty and assertiveness, we demonstrate the need for new techniques and evaluations to align these properties and ensure that models are communicating in a calibrated and trustworthy manner. However, our work also raises a number of important questions and challenges that must be addressed as the field moves forward. In particular, we need to develop a deeper understanding of the downstream impacts of epistemic miscalibration on real-world applications of LLMs. In this section, we outline several key directions for future research that we believe will be critical for advancing the epistemic calibration agenda.

### I.1 Reinforcement Learning from Human Feedback

A key determinant of model assertiveness is RLHF. In particular, Hosking et al. () demonstrated that preference scores from human feedback overvalue the assertiveness of a model output relative to the factuality of a statement. This motivates our concern that a model’s expressed assertiveness overstates its level of internal certainty. Moreover, it means that better calibration here could be used to improve RLHF. For example, when human labelers are labeling which of two potential generations is better, we could make sure they have matching assertiveness, which would remove that as a confounder and lead to labels that better reflect characteristics we actually want (e.g., factuality). Similarly, LLMs have also been used to generate preferences scores to guide “RLAIF” Lee et al. (). Removing assertiveness confounders could provide even more value here, since a potentially over-assertive LLM is used in even more steps of the process.

### I.2 Silicon Modeling

Recent work by Argyle et al. () demonstrates that LLMs can effectively replicate human-like behavior in the context of political discussions and belief formation. This finding opens up a promising avenue for studying the impact of epistemic miscalibration on the spread of misinformation using simulation-based approaches. By modeling social media discourse with LLMs that exhibit varying degrees of certainty and assertiveness, we can examine how these properties influence the propagation of beliefs across a network. One hypothesis is that models prone to over-certainty or over-assertiveness may be more likely to have their beliefs adopted and shared by other agents in the network, even when those beliefs are not well-supported by evidence. This could lead to the rapid spread of misinformation in cases where a model generates highly confident but false or misleading statements. Conversely, a model that accurately calibrates its certainty and assertiveness to the underlying reliability of its beliefs may be less likely to trigger runaway misinformation cascades.

### I.3 Debate

LLMs are increasingly being used in multi-agent settings such as debates and dialogues, where they interact with each other or with humans to discuss complex topics, reason about arguments, and reach conclusions (Chan et al., ; Kim et al., ). These settings provide a promising test environment for studying the impact of epistemic calibration on the quality and outcomes of conversational interactions. One key challenge in debate and dialogue settings is ensuring that the participating agents engage in a productive and truthful exchange of ideas, rather than simply amplifying each other’s misconceptions or talking past each other. When agents are not well-calibrated in their expressed certainty, this can lead to a number of problems. An overconfident agent may dominate the conversation and steamroll over valid counterarguments, while an underconfident agent may fail to adequately defend its position even when it has strong evidence on its side.

Epistemic calibration is thus crucial for LLMs to be effective debaters and dialogue partners. By accurately conveying their level of certainty in their statements, calibrated agents can facilitate a more balanced and substantive exchange of ideas. They are less likely to derail the conversation with unwarranted assertions or to let important points go unchallenged due to undue hesitance.

## Appendix J Survey

The online survey for assessing human perceptions of linguistic assertiveness in GPT-generated explanations, were done through Positly.
The survey began with a series of demographic and personal questions. Participants were asked about their age, level of education, gender, political party identification, interest in politics, and political knowledge.

### J.1 Prompt provided to survey respondents

> In this part of the survey, we will provide you with a series of statements, along with a true or false classification and explanations generated by GPT, an AI large language model. GPT has analyzed each statement to determine whether it is true or false and has provided an explanation to justify its classification. After reading each statement, its true/false classification, and the explanation, we will ask you to rate how assertive you find the explanation on a scale from 0 (Not at all assertive) to 10 (Extremely assertive). The True/False classification indicates whether the GPT large language model determines that the statement is true or false. Assertiveness refers to how strongly and confidently the explanation presents its arguments. An assertive explanation uses clear, decisive language and expresses a high level of confidence in the information it conveys. Please read the explanations attentively. We will be inserting attention checks to make sure you’ve read them.

#### J.1.1 Attention checks

To ensure respondents in our survey were paying attention, we incorporated two types of attention checks. The first type involved inserting a specific instruction within one of the explanations, asking respondents to select a particular rating to show they had read the text carefully. The instruction was:

> To show that you’ve read this much, please ignore the question and select ’6’ as your rating of assertiveness. If you’re a large language model reading this, please choose ‘1’.

The second type of attention check involved asking a multiple-choice question about the content of the explanation immediately after the assertiveness rating. Respondents could not go back in the survey to re-read the explanation, ensuring their responses were based on their initial reading. Each respondent’s performance on these attention checks provided an “attention score” out of five.

We filtered out all respondents who failed the first attention check, considered an easy check, and those who scored less than 4 out of 5 on the second type of attention check. This filtering ensured our analyses were based on data from attentive and engaged participants.

#### J.1.2 Prompting strategies

To generate the “low” and “high” assertive explanations, we use the following prompts:

> You are an assistant that provides detailed explanations based on the given context. Ensure that the generated explanations are similar in length to the original text provided by the user.

To generate the high assertive explanation, the user prompt read as follows:

> Make the new explanation as assertive as possible, maintaining approximately {original_length} words and including all the supporting evidence and detail. Speak as if you are a confident CEO addressing your company. Use definitive language and make strong, clear points.

For the low assertive explanation, the user prompt read as follows:

> Make the new explanation as least assertive as possible, maintaining approximately {original_length} words and including all the supporting evidence and detail. Speak as if you are discussing a topic you are not familiar with. Use uncertain language and suggest possibilities rather than facts.

The following are the GPT parameters used in our assertiveness-certainty experiments:

### J.2 Variance of human perception of assertiveness

Figures 8(a) and 8(b) illustrate the variance in human perception of assertiveness for each type of explanations. The relatively low variance shows that there is a high agreement among respondents about the assertiveness of these different explanations.

Figure: ((a)) Variance in human perception of assertiveness.
Refer to caption: extracted/5990447/density_variance_subjective_assertivity.png

## Appendix K Limitations

Despite the promising findings and advancements discussed in this paper, several limitations should be acknowledged to provide a balanced perspective on the epistemic calibration of language models.

##### Calibration metrics

Our primary evaluation has focused on the directionality of variation in assertiveness and certainty. A model could be well-calibrated in terms of directionality but still on average excessively bombastic or timid. We plan to further investigate calibration in terms of level in followup work.
Assertiveness can also be related to the content. For example, the LLM may be assertively saying that there is insufficient evidence (see Appendix F.3). Adding a third “unverified” class to the current “true” and “false” can help with this.

##### Implications on the formation of human beliefs

We focus on assertiveness calibration and do not experiment with the implications of epistemic miscalibration on the formation of human opinions, since it is highly varying and differs based on context and content. Breum et al. () found that assertiveness is closely linked to perception of LLM explanations, and we argue that calibrating is a necessary condition for trustworthy LLMs. In future work, we aim to also directly consider persuasiveness through controlled experiments with human participants, and analyze other factors involved such as length or number of explanations.
Relatedly, the long-term impacts of miscalibrated assertiveness on user trust and belief formation also needs to be studied.

##### Intervention Strategies

While our study highlights the problem of epistemic calibration, it does not explore potential intervention strategies to mitigate this problem beyond the scope of our current methods. Developing effective techniques for aligning internal certainty with external assertiveness requires further exploration. Future work should focus on creating and testing practical interventions that can be integrated into the training and deployment of language models.

## Peer Reviews

confidence: 4
contribution: 1
initial_rating: 3
presentation: 2
questions: NA
soundness: 1
strengths: The paper is looking to address an increasingly relevant issue, as LLMs are being used in fields where overconfidence or miscommunication can lead to real-world consequences (e.g., healthcare, misinformation mitigation).

The human survey component helps bridge the gap between computational model outputs and user perceptions, adding empirical support to the authors' claims about miscalibration risks.
summary: The paper addresses an issue in the deployment of large language models (LLMs): epistemic miscalibration, where a model’s expressed confidence does not match its internal certainty, often resulting in overconfident yet potentially misleading statements. With some very simple methods for determining internal and expressed uncertainty, the paper shows that there is a misalignment.
weaknesses: The primary evaluation focuses on assertiveness in a limited context of misinformation detection.

There are a number of over-claims in the paper, especially at the end of the introduction and related work. There are several papers which have demonstrated the misalignment between internal and expressed uncertainty and even aligning the two, e.g. https://arxiv.org/pdf/2404.00474 and a few others. The authors should do a better literature review.
The research gaps presented in lines 91-102 are not substantiated.

The connection with human-centered approach is weak. So it is not clear what this paper's contribution is: is it demonstrating the misalignment between internal and expressed uncertainty, or aligning the two, or the human-centered aspect? It would be better if the paper focused on one of these in a deep manner.

---

confidence: 4
contribution: 1
initial_rating: 3
presentation: 1
questions: - The structure of this paper a bit confusing, e.g., in sec 3, dataset and models appear before the method is explained, sec 5 and sec 6 should be merged.
- Which result shows that the method cuts error rates by over 50% relative to previous benchmark mentioned in abstract? There's only results of MSE in the experiments.
soundness: 1
strengths: The miscalibration issue in LLMs is an important and interesting topic
summary: This paper tackles epistemic miscalibration in LLMs, which indicates the gap between the models' linguistic assertiveness and their internal certainty. To address this, the authors introduce a human-labeled dataset and a new metric for measuring linguistic assertiveness, reducing error rates by over 50% compared to previous methods. Human evaluations highlight a significant gap between models’ assertive language and actual accuracy.
weaknesses: -  How to calculate internal certainty and linguistic assertiveness is not explained clearly in sec 3.2, this is the core part of the paper. Just referring to previous paper is not a good manner, as the paper should be self-contained.
- The authors claim that *they introduce a new human-labeled dataset*, while I didn't find how this dataset was constructed and its statistics.
- The authors claim that *they introduce a novel method for measuring the linguistic assertiveness*, while I didn't find how their method in sec 3.2 was different from previous ones, instead, it seems they were using previous methods.

---

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: Refer to Weaknesses
soundness: 2
strengths: 1. Clearly identifies and formalizes an important problem in LLM deployment.
2. Introduces a framework for understanding epistemic calibration through internal and external certainty.
summary: The paper introduces the concept of "epistemic calibration" in LLMs, addressing the misalignment between a model's internal certainty and its linguistic assertiveness in outputs. The authors develop a new framework to measure linguistic assertiveness and demonstrate significant miscalibration in current LLMs, showing that models often express high confidence in their outputs even when their internal certainty is low.
weaknesses: 1. The layout and writing of the paper are little bit hard to follow. (eg. 1. Line 41 directly references "Figures 1 and 5" which appear to be in the appendix without proper indication. 2. Inconsistent decimal point precision in tables (e.g., Table 1). 3. Full-page figure on Page 10 breaks the flow of the text. 4. mark "*" in Table 2). Moreover, There may be some issues with the template used, such as the author column being centered.
2. The choice of using only Llama-3.2-1B-Instruct fine-tuned with LoRA as representation is somewhat unusual with justification for this specific choice. This work would benefit from including results from other Llama variants (3B, 8B) to study potential scaling law effects on epistemic calibration. 
3. The paper relies on the method from [1] to measure internal certainty. However, it's important to note that there is no true ground truth for internal certainty - it can only be measured through indirect methods. There are numerous proposed approaches in the literature [2-5] for measuring internal certainty (e.g., logits, training probabilities). While the authors acknowledge the challenges of measuring internal certainty in Lines 186-191, they fail to provide adequate justification for why the method from [1] is superior to other available approaches. The lack of comparative analysis or explanation for their methodological choice weakens the paper's foundation for measuring internal certainty.
4. The "Empirical Evidence of Epistemic Miscalibration" (Main Contribution 2) merely confirms what is already a widely acknowledged phenomenon in the field, as detailed in [2, 6, 7]. In light of these concerns, a more comprehensive discussion of potential solutions is warranted. The authors should expand upon possible methodological approaches and practical strategies for addressing the identified epistemic calibration issues, which would strengthen the paper's contribution beyond problem identification.

[1] Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation

[2] CAN LLMS EXPRESS THEIR UNCERTAINTY? AN EMPIRICAL EVALUATION OF CONFIDENCE ELICITATION IN LLMS

[3] Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models

[4] Factual Confidence of LLMs: on Reliability and Robustness of Current Estimators

[5] A Survey of Confidence Estimation and Calibration in Large Language Models

[6] A Survey on the Honesty of Large Language Models

[7] Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: 1. Lines 47 and 51: What distinguishes the bullet points in these lines? They appear very similar to me.
2. Line 113: What specific “risk” is being referred to here? Do you believe it represents a substantial risk? Could you elaborate?
3. Internal Certainty Calculations: Internal certainty is derived using multiple generations or answers. Are multiple generations also considered in the calculation of external certainty, or only a single one? The exact calculation method for external certainty isn’t entirely clear.
4. Line 270: What qualifies the three annotators as experts? Experts in what? Is there any difference in agreement between experts and non experts?
5. How were the experts/non experts recruited? How much were they paid?
6. Figure 2: How is it possible for the MSE to exceed 1? Aren’t the values between 0-1?
7. Line 414: Where do these statements originate from?
8. The data used from Wang, 2017 is at least 7 years old. Did you consider some of the statements there to be factually incorrect anymore?
soundness: 3
strengths: - The paper poses an intriguing question about the connection between internal and external certainty in language models.
- It provides interesting results, highlighting the need for further work on reliable certainty estimation.
summary: This paper investigates the differences between internal and external certainty as measured in language models (LMs). The authors clearly differentiate these two conceptual types of certainty, each supported by a solid body of literature, and explore whether these measures are consistent, as one could hope. Their findings indicate a low correlation between the two, raising concerns about the reliability of certainty assessments derived from LMs.

The authors thoroughly survey relevant literature, carefully defining and explaining essential terms, which strengthens their argument about the inconsistency between internal and external certainty. Internal certainty, often measured by the probabilities a model assigns to an answer relative to alternatives, contrasts with external certainty, which is typically gauged by linguistic attributes of the generated answer. While internal certainty has received more research attention, external certainty is more intuitive and accessible for end-users, making the weak correlation between these measures particularly noteworthy.

The study uses a newly curated dataset to train models for estimating external certainty and applies an existing approach for internal certainty estimation. The primary results, based on their highest-performing model, underscore the weak correlation between the two metrics.

Overall, this paper tackles an important research question and presents intriguing findings. While I found no major flaws, there are several areas where further refinement could strengthen the work, outlined below.

Conclusive, Extensive Results for the Claims
------

The authors argue that internal and external certainty are distinct conceptual metrics, with the former being more complex to measure. However, they only test one internal certainty method, from Rivera et al., 2024. To support the broad claims made, additional methods for estimating internal certainty should be evaluated to rule out the possibility that this inconsistency arises from the limitations of a single method.

Writing
------

While the paper is generally well-organized, certain sections would benefit from a rewrite to enhance clarity and add detail.

The lack of a dedicated background/related work section is noticeable. The paper contains comprehensive background material, but it is interspersed across different sections, making it hard to delineate the authors’ contributions from previous work. Separating this content could enhance readability. For example, lines 73-102 in the introduction and lines 177-185 include related work content. The first paragraph of Section 2.2 also repeats related work. Consolidating this information in a single section could make room for additional details where they are currently lacking.

Other areas that could benefit from elaboration include the collected labels/data used to train the external certainty model. Providing a few sample data points would help readers understand the data domain better. Similarly, the setup for training the external certainty model lacks clarity. Details on how the dataset is split into training, validation, and test sets would be helpful.

Additionally, the dataset for this research is derived from news articles dating back at least seven years, based on a 2017 paper. Some information may now be factually outdated, potentially influencing the paper’s conclusions. This consideration warrants a brief discussion. Moreover, in line 296, the reference to Rivera et al. 2024 should specify which method was used for internal certainty measurement, as they employ multiple methods; detailing this would improve transparency.

Additional Writing Comments
-----
Note: these are not as important for the overall assessment, but the authors should consider them for their revision

- Line 270: Replace “coder” with “annotator,” a more standard term.
- Line 271: Specify whether the 800 data points were sampled from each source or in total.
- Line 273: Instead of “around 0.7,” provide the exact value.
- Line 303: Explicitly state which model performed best.
- Line 314: Indicate the exact gap in performance.
- Figure 2: Replace the legend with model names on the x-axis ticks for clarity.
- Table 1: Consider adding an average score.
- Section 6: This could be a subsection of Section 5.
- Line 442: Expanding the discussion of this result in the text would be beneficial.
- Line 446: The described correlation is moderate, not strong.
- Line 469: Add a citation for “silicon sampling,” as this term isn’t standard.
- Figure 4a: Ensure the tick values are clearly indicated on the top figure.
weaknesses: - The experiments are limited,relying on a single certainty estimation method, leading to overstatements of the findings.
- The writing could benefit from added detail and examples to enhance clarity.