Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
Authors: Kaitlyn Zhou
Dan Jurafsky
Tatsunori Hashimoto
Venue: EMNLP 2023 Main (2023)
Keywords: expressions of uncertainty
analysis of language models
TL;DR: Expressions of certainty in prompts hurt the accuracy of language models and we trace this effect back to pretraining datasets.

Abstract: The increased deployment of LMs for real-world tasks involving knowledge and facts makes it important to understand model epistemology: what LMs think they know, and how their attitudes toward that knowledge are affected by language use in their inputs. Here, we study an aspect of model epistemology: how epistemic markers of certainty, uncertainty, or evidentiality like "I'm sure it's", "I think it's", or "Wikipedia says it's" affect models, and whether they contribute to model failures. We develop a typology of epistemic markers and inject 50 markers into prompts for question answering. We find that LMs are highly sensitive to epistemic markers in prompts, with accuracies varying more than 80%. Surprisingly, we find that expressions of high certainty result in a 7% decrease in accuracy as compared to low certainty expressions; similarly, factive verbs hurt performance, while evidentials benefit performance. Our analysis of a popular pretraining dataset shows that these markers of uncertainty are associated with answers on question-answering websites, while markers of certainty are associated with questions. These associations may suggest that the behavior of LMs is based on mimicking observed language use, rather than truly reflecting epistemic uncertainty.

Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models
Kaitlyn Zhou
Dan Jurafsky Stanford University
Tatsunori Hashimoto Stanford University
Stanford University katezhou@stanford.cm edu
jurafsky@stanford.co edu
thashimestanfore edu
Abstract
Expressions of Uncertality
PROMPT
GENERATION
Zaro shot Injection
Verbel
What the capital of Frence? A:l think it's..
"Paris."
The increased deployment of LMs for real- world tasks involving knowledge and facts makes important to understand model episte- mology: what LMs think they know, and how their attitudes toward that knowledge are af- fected by language use in their inputs. Here, study aspect of model opistemology: how epistemic markers of certainty, uncertainty, or evidentiality like "I I' sure it's", "I think it's". "Wikipedia says it's" affect models, and whether they contribute to model failures. We develop typology of epistemic markers and inject 50 markers into prompts for question an- swering, We find that LMs are highly sensitive epistemic markers in prompts, with accu- racies varying more than 80%. Surprisingly. we find that expressions of high certainty re- sult in a 7% decrease in accuracy compared to low certainty expressions; similarly, factive verbs hurt performance, while evidentials ben- efit performance. Our analysis of a popular pretraining dataset shows that these markers of uncertainty associated with answers question answering websites, while markers of certainty are associated with questions. These associations may suggest that the behavior of LMs based on mimicking observed language use, rather than truly reflecting epistemic un- certainty.
Numeriial
capital France? A:Imm 100% certain it's..
O0
"Lyon."
Figure 1: Using zero-shot promoting to inject verbal and numerical uncertainties into trivia questions. We find drops accuracy when expressions high certainty used compared to expressions of low certainty.
Naturalistic expressions of uncertainty/ccrrainty cover a broad range of discourse acts such signal- ing hesitancy, attributing information, or acknowl- edging limitations. Prior work has focused on one aspect of this: linguistic calibration, particularly on learning the mapping between the internal proba- bilities of model and an ordinal output (Kadavath et al., 2022; Lin et al., 2022; Mielke et al., 2022). Yet epistemic expressions encompass much wider range of features than can be represented by sin- gle value, such as the sources of information, or the nature of speaker commitment, or whether a piece of knowledge is asserted or presupposed. Our work seeks to understand how the various dimensions of uncertainty like hedges (c.g., "It could be... "), factive verbs (e.g.. "We realize it it's.. "), and ev- idential markers (e.g.. "Wikipedia says it i's... ") impact language generations. By shifting our focus to naturalistic expressions of uncertainty and cer- tainty, we would enable models flexibly interpret a wider range of uncertainties otherwise not possi- ble under the current linguistic calibration setup.
1 Introduction
As natural language systems are increasingly used situations involving factuality and knowledge, it becomes important for LMs to be able to inter- pret how humans talk about knowledge. LMs must learn to accurately interpret linguistic cues like ex- pressions of uncertainty and certainty that used talk about confidence, source, and limitations of information. In this work, we seek understand how models interpret this linguistic phenomenon by measuring how language generation varies when prompted with expressions of uncertainty.
To understand how epistemic expressions affect language models, we use zero-shot prompting and inject verbal and numerical markers into trivia ques- tion prompts. This process converts a prompt like "What the capital of France?" to. "What is the capital France, think it's... (Figure 1). Aided by our linguistic typology, we then measure how different epistemic marker types impact model ac-

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curacy. By doing so, we complement current lin- guistic calibration work and paint more complete picture of the role of these epistemic markers.
Weakennrrs (Uncertairit))
Strangthenare (Certalitt)
Aen cariain Elacs
We begin with two broad hypotheses on how LMs might respond to expressions of uncertainty. First, we might suppose that models are robust to any added expressions of uncertainty in the prompt. An alternative hypothesis is that, models might re- spond differently based the uncertainty cues, and using a marker suggesting certainty or confi- dence might be more likely produce the correct response than a prompt with low certainty or con- fidence. Under the latter hypothesis, we might expect performance for a prompt with no epistemic markers (which we call the standard method) to lie in between these two. This second hypothesis would also be consistent with prior work showing that LMs can generate language in the style of di- verse personas (Lee al., 2022; Park al., 2022). Surprisingly, we find that injecting expressions of high certainty like "I'm certain it's" or "I'm 100% sure" actually leads to lower accuracy. We follow up with qualitative and quantitative analysis of popular training data, suggesting some potential sources of these unexpected behaviors. Our work thus offers three key contributions:
Approximators Wound ...
Plausibiiity Shields ttink
Fáctive Verbe
Evidentials
TTnyy eernndutggd its...
Lexical Features of Expressions of Uncer- tainty. Uncertainty classification partly adapted from (Prince et al., 1982). Certainty markers are strength- eners which contain factive verbs. Evidential markers can be both expressions of certainty and uncertainty (strengtheners or weakeners). Personal pronouns and reference sources additional dimensions of pressions of (un)certainty not shown in this diagram.
Figure 2:
group these linguistic devices into weakeners and strengtheners.
The most widely studied weakeners are hedges, first defined by Lakoff (1975) as related to modi- fying or weakening the category membership of a predicate or nominal. The central kind of hedges approximators (e.g., somewhat, kind of, about. approximately), which hedge propositional content. Another class of weakeners that some (like Prince et al. (1982)) but not others classify under hedges are plausibility shields which express a speaker's lower level of commitment (c.g., I think, I believe). Strengtheners are constructions that mark cer- lainty. We use this term to refer to strengthening speaker commitment to truth value, and also to strengthening category membership. Strengthen- ers include boosters or intensifiers (e.g.. am certain". "Undoubtedly") (Hyland, 2005, 2014).
We introduce typology of expressions of uncertainty evaluate how linguistic features impact LM generation.
We demonstrate how model accuracy suffers when expressions of certainty (e.g., "I'm cer- tain") are used.
We perform qualitative and quantitative anal- ysis to reveal the potential origins of these unexpected behaviors.
While boosters can assert certainty or truth, a second kind of strengthening construction, the fac- tive verb, is used to presuppose certainty or truth. Factive verbs like "know". "realize". "under- stand" (Kiparsky and Kiparsky, 1970) presuppose the truth of the complement sentence. The state- ment "X realizes Y" presupposes that Y is true, (and also asserts that is aware of it). By contrast, "X believes Y" makes no presupposition about the truth of Y. Thus the sentence "He realizes [Madrid is the capital of France]" is infelicitous, because "realize" as factive verb presupposes the truth of its complement clause, which in this case is false (Madrid is not the capital of France).
Together, our findings illustrate the shortcom- ings of models' abilities to interpret expressions of uncertainty and highlight the gaps that still exist between natural and generated language.
2 Expressions of Certainty and Uncertainty: Linguistic Background
A broad linguistic literature exists in epistemolog- ical markers related certainty and uncertainty, both for strenghhening/weakennng category mem- bership and strengthening/weakkiii speaker com- mitment to the truth value of proposition (sce Related Work). For convenience, we broadly
A third class of markers can mark both certainty and uncertainty by directly indicating the source the information. Such expressions (e.g., "Accord-
Details: http:///tthhb..omm/cccchhaaaiiig thennnggtthhheeeeeeeeeeeee grey

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ing to Wikipedia". "I heard", "I saw") are called evidential markers, linguistic signals that tells the hearer where the information came from (Aikhen- vald, 2004). One subtype of evidential markers, quotative markers, are used when reported informa- tion overlly references source (c.g.. "According to research in the latest issue of Nature", "Two recent studies demonstrate that... "). We examine when references citing source (e.g., "Wikipedia says")) versus when the source is unspecified or very indirect (e.g., "They said"). We'll refer this former case as sourced as a shorthand for indicat- ing that a source is mentioned.
mated would take users a minute or two com- plete the task, paid users $0.35 USD for the task which results in roughly $10.50 USD to $21.00 USD an hour. We collected a total of 9 samples of 5 examples each. The authors then read, filtered, and modified the examples to follow the overall lin- guistic structure of the other templates. This study did not require IRB approval as it is not study of humans and we do not collect any information about the annotators themselves.
3 Methods
To study how models interpret uncertainty, we in- ject markers into trivia questions in open-ended qmestion-anssering Using our typology, we create fifty sentences (minimal pairs) for every question.
Finally, first-person personal pronouns (e.g., "I", "we", "our") can be used to mark subjectivity and uncertainty in expressions like "I think".
Our datasets include TriviaQA, a standard QA dataset (Joshi al., 2017); Natural Questions (closed-book), an aggregated set of Google queries (K (Kwiatkowski et al., 2019a); CountryQA, which we constructed using names of countries and capitals in the form of "What is the capital of A Afghanistan?"; and Jcopardy questions crawled from fan- aun-created database, J! Archive." 3 We use random subset of 200 questions for three of our datasets. For CountryQA, we used all 53 questions whose answers were in vocabulary (details in 3.2). Although the total number of questions per tem- plate is small, we present our key findings in aggre- gate across all templates with shared characteristics, increasing our statistical power. We test each of our fifty templates across this subset of questions and calculate 95% confidence intervals using bootstrap resampling t-tests.
2.1 Expressions of Uncertainty Typology
Using a bottom-up process, we gathered a diverse set of templates guided by the linguistic literature to develop a single, coherent taxonomy and clas- sify templates. To gather the templates, the authors relied the literature from Prince al. (1982), brainstormed additional templates, and used crowd sourcing via Amazon Mechanical Turk. The au- thors then drew on linguistic literature to aug- ment this list, adding dimensions such as factive verbs (Kiparsky and Kiparsky, 1970), evidentials (a broader category of attributions) (Aikhenvald, 2004), and authoritative sources. Finally, they inte- grated this literature, developed coherent taxon- omy, and classified each of our templates. Figure 2 illustrates how these markers relate certainty and uncertainty in our coding scheme.
The final list of templates includes: weakeners, strengtheners, plausibility shields, factives, eviden- tial markers, mentions of sources, and personal pronouns (Appendix Table 6). Each expression is then coded for the linguistic features above, allow- ing us to analyze how epistemological modifiers impact language modeling in the QA setting.2
We primarily study OpenAT's GPT models they are commonly used large language models with reasonable performance on QA tasks (Liang et al., 2023). For all models, except GPT4 we set temperature to 1. For the generated tokens, we take the sum of the probability assigned to the gold answer(s) to be the probability-on-goe culating accuracy, we generate 10 tokens and if any of the tokens match the answer (or aliases of the answer), we'll ll count that as correct generation. This is done to not unfairly disadvantage templates that are prone to generating words prior to emitting the answer (e.g., "Allegedly, it's said to be..."), "). For GPT4, where the log probabilities were not
When cal-
2.2 Amazon Mechanical Turk Details
We use Amazon Mechanical Turk to crowd-source additional expressions of uncertainty (Figure 7 in Appendix). Workers were filtered to be have HITs greater than 99 and to have at least 500 approved HITs. Given the simplicity of the task, we esti-
Approximatoos, which are primarily used for expressing uncertainty of continuous items are excluded from our tem- plates as we are primarily working with trivia questions with discrete answers.
hatpss//j-archive..o
"Authors also manually inspected 1,000 answers to ensure this approach doesn't 't lead to false positives e.g. "The answer is not Paris."

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available, we set the temperature O, also ensur- ing a (mostly) deterministic oulput, and used the generated text.
4 The Impact of Uncertainty on Language Generation
Information in real-life text is rarely in black-and- white, and expressions of uncertainty are neces- sary in supporting decision-making processes. In this section, we investigate how GPT3's generation changes based on the verbal expressions of uncer- tainty in its prompt (Figure 1, top) and whether some expressions of uncertainty can have system- atic effects on model behavior.
3.1 Additional Prompting Details
For Section 4, we OpenAl's researcher API and retrieve the top 50 most probable predictions per token. For Section A.1 we use the standard APl and retrieve the top most probable predictions per token.
We are careful that our prompts do not end with trailing white space (" ") as recommended by Ope- nAl in order to prompt the best generations. We also use the delimiters "Q:" and "A:" to signal the
4.1 Variation in GPT3 Responses Across Verbal Uncertainties
We evaluate GPT3 (davinci) using zero-shot prompting on our four datasets. We find that the results do not support our first hypothesis: GPT3 is highly sensitive to uncertainty cues, and accu- racy changes significantly depending on the cer- tainty/uncertainty expression used. Across our datasets, we find that accuracies can change by up to 80% on the exact same set of questions. This is especially pronounced in Country where a template like. "We realize it's... achieves 14% accuracy while many other templates result in per- fect accuracy. In TriviaQA, when prompted with template such "I m certain it's... "hhe model's accuracy is 42% but when prompted with "I would need to double check but maybe it's... accuracy increases to 56%. Our findings illustrate that ex- pressions of uncertainty affect language generation and that the changes resulting from these expres- sions have substantive impact on overall accuracy. Turning to our second hypothesis, we seek to understand how GPT3's responses QA answers change based the expression of uncertainty used in the prompt. Surprisingly, we find that weaken- ers perform significantly better than strengtheners across all four of our datasets. The average accu- racy among weakeners across all four datasets is 47% compared to 40% among strengtheners. This effect is especially large in CountryQA where the accuracy gap is 17%. This effect is driven by the use of factive verbs in strengtheners (as nearly all uses of factive verbs in our templates are strength- eners) and the use of factive verbs consistently results in significant losses accuracy (Figure 3). In other words, when the template presupposes the truth, accuracy drops.
start and end of questions and answers.
Lastly, for additional transparency and given the frequent changes in models, we also provide times- tamps of when the models were prompted. We used the Davinci model from Feb 2023 on all four datasets. Experiments on Ada, Babbage, Curie, text-davinci-03, and GPT4 models on the Trivi- aQA dataset were all conducted in June of 2023. Experiments on Ada, Babbage, Curie, text-davinci, and GPT4 models on the Country QA, NaturalQA, and Jeopardy datasets were conducted the week of August 28th, 2023.
3.2 Challenges in Open-Endee Generation
A key challenge of open-ended generation is mea- suring log probabilities of generated tokens, needed for our analysis. Answers can be paraphrased in myriad of ways, making the scoring of tokens diffi cult. Furthermore, some answers are OOV which necessitates multi-subtoken generation. To avoid these potential confounders, we filter out questions with word or OOV answers, allowing us to more accurately measure the probability placed on the gold answers.
The filtering of questions has the added advan- tage of prioritizing questions with freuucryy answers. By focusing on questions with high- frequency answers, the resulting dataset contains questions for which LMs are more likely to know the right answer. This helps mitigate the effects of performance variation that may result from wide range of question difficulty as well as demonstrate that our observed effects are not isolated unusual or rare questions.
*In CountryQA QA and TriviaQA, an additional new line char- acter was added before "A:". To reduce accruing additional costs, NaturalQA and Jeopardy results were not rerun to match this exact template.
This finding contradicts the second hypothesis, as we might have expected expressions of certainty
"The exception heing "I vaguely remember it's.....

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Accuracy by Factive Verbs
Accuracy by Evidential Marker
1.0
Factive
Evidential
Evidential
0.8
0.8
Accuracy
0.6
Accuracy
0.4
0.2
0.0
CountryQA
Jeapardy
NaturaiqA
TriviQA
CountryQA
Jeopardy
NaturalQA
TIMaQA
Datasets
Figure 3: Significant and consistent accuracy losses for templates with factive verbs (left). Evidential markers significantly improves accuracy in three of four datasets (right). 95% CI calculated using bootstrap resampling. Visualizing results for GPT-3 (davinci).
ada
babbage
curie
davinci
instruct
gpt-4
Boosters
0.091
0.257
0.313
0.392
0.589
0.793
Hedges
0.079
0.272
0.333
0.468*#*
0.642***
0.822###
Factive Verbs
0.078
0.237
0.293
0.347
0.555
0.771
Non-Faatives Verbs
0.085
0.276****
0.336
0.468 ***
0.641 ***
0.821 ***
Evidentials
0.087
0.281 ***
0.347*##
0.449*
0.640
0.820*#*
Non-evidentials 0.080
0.250
0.301
0.433
0.601
0.799
Table 1: Across all six models tested, hedges outperform boosters, non-factive verbs outperform factives and evidentials out-perform non-evidentials. (Instruct = text next-dvvinci-00. GPT4 uses context window 32K.) 1-test p-values, < 0.05, ** < 0.01, *** 0
to improve performance, not hurt it. This is par- ticularly concerning confident prompts wwhich LM users might naturally expect to result in better generations- actually lead worse generation. Furthermore, we find that in three of our four datasets, the use evidential markers significantly improves performance. fact, some the best performing templates include evidential markers with source. This is also consistent with recent work showing how the grounding of prompts in- creases model accuracy in generation (Weller et al., 2023). The top ten performing prompts for each dataset are listed in Tables 7. and 10.
verbs result in significant decreases to model per- formance and meanwhile evidentials markers sig- nificantly improve performance (Table 1).
4.2 Expressions of Uncertainty Compared to the Standard Prompting Method
Lastly, we find that the use of expressions of un- certainty might actually lead to better performance than the standard prompting method (i.e., just sim- ply using "Q: <question> A:"). In TriviaQA the template "Online says it's... "cchieves an accuracy of 66% compared to 63% achieved by the standard method. Natural Questions. there are seven tem- plates that outperform the standard method, six which are expressions of uncertainty. Using our results with six models across all four datasets, we aggregated the results by template and identified six templates which perform significantly better (!-test; p-value 0.05) than the standard method (Appendix Table 11) These promising results sug- gest that including uncertainty may not only help human decision makers, may also improve the absolute accuracy of the model.
The results across the other linguistic features are mixed. Across the four datasets, there is not a consistent improvement from the use of plausi- bility shields, sources, or personal pronouns (See Appendix).
For generalizability, test five additional mod- els (GPT3 Ada, Babbage, Curie. text-davinci-003. GPT4 (32k) on the TriviaQA dataset (n=200) with our fifty templates and find our results reproduce. Across almost all models, boosters and factive

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5 Why Does Certainty Hurt?
Furthermore, find that weakeners led to flat- tening of the distribution of probability mass across answers, compared to strengtheners. We look at the entropy of the probability distribution of top to- kens not counting the top prediction; essentially the uncertainty among all but the top candidate. This entropy is significantly higher among weakeners than strengtheners (Table 2). Our finding suggests that the increase in accuracy of weakeners is not due an increase in answer confidence, but rather when weakener is used, the model responds by placing probability more evenly across cach of the remaining possible options.
What explains our surprising finding that templates with weakeners outperform templates with strength- eners? Here, we discuss and evaluate hypotheses and potential confounders.
5.1 Certainty Affects Performance Independent of Perplexity
Recent work from Gonen et al. (2022) discusses how perplexity, as proxy for frequency, explains variation in quality of generations. Phrases with high perplexity result in significant drop in perfor- mance when used as prompts in language modeling. We test if perplexity could be a confounding vari- able in our experiments but find that the perplexity of our templates is in fact not correlated with the accuracy of the prompts (Pearson's 's -0.03) (See Appendix). These results validate that the varia- tions from prompts are not caused by trivial factors such text length or the frequency of the expres- sion in training data.
5.3 Certainty Used in Questions Instead of Answers
Why is it that expressions of certainry lead to low- ered performance? We look for potential explana- tions by examining expressions of uncertainty in language model pretraining data. We queried for expressions of uncertainty like "I'm certain it's" or "I'm it's" in The Pile (Gao et al., 2020), popular pretaining dataset, and in a qualitative exploratory analysis, found that certainty was often being used in questions rather than answers.
5.2 A Redistribution of Probability Mass When Prompted with Weakeners
Could it be that weakeners are changing the un- derlying probability distribution of the potential answers? If weakeners change the answer distribu- tion, we might expect weakeners to induce an in- crease in the probabilityy onngood (which is defined as the sum the probabilities placed on all of the answer aliases). Focusing on GPT3 (davinci), we calculate the average probability-on-gon among all correctly predicted answers and find this is not the case. In fact, the grobability-on-gan plates with weakeners is slightly lower than the probability-on-gue from templates with strength- eners. This is true across three of our four datasets NaturalQA (42% vs 45%), JeopardyQA (47% vs 51%), and TriviaQA (53% vs 55%).
To quantitatively test this hypothesis, we mea- sure the volume of expressions of uncertainty in the Stack Exchange section of the Pile with a set of un- certainty expressions. To our surprise, expressions of certainty, "I'm sure" and "It must be", occur less than half as often in answers (104 instances per million words) as in questions (280 instances per million words). Conversely, expressions of un- certainty, "It could be" and "Maybe ir's", occur about twice as often in answers (436 instances per million words) as in questions (222 instances per million words). In other words, those asking for in- formation are using expressions of certainty while those with the answers (or attempting to answer the question) are using expressions of uncertainty (details and statistics in Appendix).
from tem-
Dataset
weakeners
strengtheners
TriviaQA
2.980 +: 0.01
2.917 +:2 0.01
A number of factors could be contributing to this phenomenon, including the use of uncertainty to express politeness (Brown and Levinson, 1987) or the use of certainty to rule out options, or the use of certainty to establish group membership (Hyland, 1998). To give an estimate of the prevalence of these acts, we perform qualitative open coding on 100 samples of two expressions. question posts, the high certainty expression "I'm sure", used 34 times to establish group membership/admit ig-
CAuntryQA
3.078 = 0.02
2.875 + 0.03
Jeopardy
3.170 + 0.01
3.089 + 0.01
NaturalQA
3.167 = 0.01
3.106 + 0.01
Table 2: Average entropy of the probability distribution alternative tokens among weakeners and strengthen- ers. Across all four datasets, entropy is higher among weakeners, an indication the model places probability more evenly across the alternative answers. 95% CI calculated using standard error.

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norance (e.g.. "I'm sure it's simple error") and used 22 times isolate an issue (c.g., "I'm sure am catching right"). In answer posts, the low certainty expression think", is used 25 times to politely give instructions and corrections (e.g.. "/ think this should work for you.... and "I think you meant to write... "). These instances reveal the ways in which humans may be using expressions of certainty that go beyond expressing epistemic certainty. Our analysis of pretraining data suggests that language models might be mimicking this be- havior and responding to prompts with epistemic markers in this surprising but explainable way.
answer 57% of the time (Figure 4). Formally, we evaluate the expected calibration error (ECE) and find poor values ranging from 0.50 to 0.30, 0 being the best.
Second, consistent with our findings from Sec- tion 4, we find that certainty does hurt model per- formance. However, we find this is true only at the extremes. We see performance on models peaks usually between 70% and 90% in numerical val- ues but drops in accuracy when using 100% in the prompt. Across all four datasets, with seven tem- plates each, 21 out of the 28 templates which use 100% numerical values had a lower probability- on-gold than templates which used 90% numerical values (See Appendix). Additionally, at the other extreme, when 0% is used in templates, there is also drastic drop to accuracy.
6
The Impact of the Degree of Uncertainty on Performance
Results from Section 3.2 illustrate that GPT3 is highly sensitive to uncertainty in prompts, and cer- tainty seems to lead to diminished accuracy. Here, we extend these results to study uncertainty at more fine-gre fine-grained level, allowing us to ask if the degree of uncertainty could play a role in the accu- racy of model generation.
Why Does 100% Certainty Hurt?
We hypothesize this drop in accuracy might be the result of the use of hyperbolic or exaggerated lan- guage in the training set, in which numbers are used non-literally. When someone says "I'm 100% cer- tain there was pie left", they don't mean they are 100% certain is there pie but rather emphasiz- ing their strong belief there was pie. Again, we turn to the Pile and qualitatively analyze over 500 exam- ples of use cases of "100%". ". We remove other uses of " "100%" such as in code (e.g., "width:10%% 100%") and sample 50 instances from both question and answer posts. We find, surprisingly, that 100% is in fact often used to express uncertainty.
Introducing Numerical Values Here, we intro- duce numerical values into our verbal expressions of uncertainty. The setup of our task changes from "What is the capital of Belarus? I'm sure it's... to "What is the capital of Belarus? I'm 90% sure it's...".. We use set of seven expressions covering a range of numerical uncertainty expres- sions, including those that use personal pronouns to weaken uncertainty ("I'm 90% certain..." ") and those which indicate uncertainty probabilisticaly but without mentioning the self ("70% chance it's it's...... We also downsample our test set to 50 questions per dataset and evaluate each template 0%, 10%, 30%, 50%, 70%, 90% and 100% inter- vals.
Of our 100 samples, 44 instances are used with negation (e.g., "never be 100% accurate" or "is not always 100% reliable") and half are expressions of uncertainty (e.g., "I'm not 100% sure" or "I don't 't 100% follow"). In questions, the rate of expressions of uncertainty was nearly double that of answers (14 vs 8). In contrast, only 3 instances in our sam- ple indicate 100% certainty (e.g., "I'm 100% sure that"). We hypothesize that both the use of nega- tion with "100%" and the general lack of use of "100%" " with expressions of certainty contribute to the lowered performance of these prompts.
100% Certainty is not 100 % Accurate
Our findings for numerical uncertainties extend our earlier analysis by enabling us to obtain more fine-grained results on whether a model is linguis- tically calibrated, and how much certainty causes performance degradation.
First, we find that numerical uncertainties in the prompt are not well calibrated with the accuracy of the answers generated." 7 E.g.. the prompt "I'm 90% sure it' s... in TriviaQA only produces the correct
Another confounder in how model's interpret numerical values could be the distribution of nu- merical frequencies in typical model training data. Querying the Pile, we find that there are drastic imbalances in the use of percentages in training datascts. There are significant spikes in frequency at the upper extremes (50%, 95% and 100%) (Fig-
"Not this is slightly different from calibration in prior work which calibrates between the model's confidence and accuracy.

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CountryQA
TriviaQA
10.6
30.0
70.0
0.975
Accuracy
0. 0.950
Accuracy
0.0
0.50
0.925
100.0
0.900
0.45
0.875
Mean
100..0
Mean
20
60
80
100
0
40
80
100
Percent
Percent
NaturalQA
Jeopardy Questions
0.28
Mean
0.400
10.0
00.0
Accuracy
0.26
70.0
0.375
30.0
10.0
30.0
50.0
0.24
100.0
0.22
0.325
100.0
0.20
0.0
Mean
20
60
100
20
40
60
80
100
Percent
Percent
Figure 4: The X-axis indicates the percentage that was injected into the verbal uncertainty. The Y-axis indicates the accuracy across numerical uncertainties. Note the consistent drop in accuracy between 90% and 100% uncertainty and the increase in accuracy between 0% and 10% uncertainty.
ure 5). This might be happening more common to express (e.g.. "100% organic") found more often in code. Humans might also naturally exaggerate values or use colloquial terms to describe confidence. The presence of scientific language like "95% confidence interval" could be another possible source of imbalance. Although spoken natural language also includes rounded per- centages, the use of only textual data might be further exacerbating this bias.
values
(between model confidence and accuracy) (Jiang et al., 2021: Desai and Durrett, 2020; Jagannatha and Yu, 2020; Kamath al., 2020; Kong et al., 2020). However, the community has found mixed results on the calibration of neural model (Min- derer et al., 2021; Carrell et al., 2022); for ex- ample, Desai and Durrett (2020) shows that pre- trained transformers are relatively well-calibrated meanwhile Wang al. (2020) found severe mis- calibration in neural machine translation. Another line of work also explore the trade-off between model performance and calibration (Stengel-Eskin and Van Durme, 2023).
Frequency of Percentages in the Pile
counts
Requence
40000
Closest to our work, Mielke et al. (2022) pro- pose solutions to reducing model overconfidence through linguistic calibration, Kadavath et al. (2022) experiment with models' ability emit self- confidence after finding that models are relatively well-calibrated, and Lin et al. (2022) teach mod- els to be linguistically calibrated when answering math questions.
20000
11 m du mi domnnnaon unlmn mnban mn mn malll
10
65
1np.
Percert
Figure 5: Visualization of the Frequency of percentages found in the pile. Note the peaks at the extremes, (0, 50, 10), and peaks at every 10 and 5 intervals from the first million samples queried from the Pile dataset using the HuggingFace API.
In addition, semanticists and computational lin- guists have long studied speaker commitment fac- tors such as factivity (Karttunen, 1971; Degen and Tonhauser, 2022) and projection (Simons et al., 2010), and more recent work include corpora like the CommitmentBBnk (De Marneffe al., 2019) which offers naturally occurring examples, as well as new experimental paradigms to investi- gate speaker commitment (Degen et al., 2019). A wide variety of scholars have examined computa-
8 Related Work
While scholars have studied model uncertainty, prior work has focused on more accurately ex- tracting model confidence (Kuhn et al., 2023; Sun al., 2022; Gleave and Irving, 2022), measuring (Kwiatkowski et al., 2019b; Radford et al., 2019; Liang et al., 2023) and improving model calibration

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tional issues in factuality, veridicality, and commit- ment (Saurí and Pustejovsky. 2009; de Marneffe et al., 2012; Stanovsky et al., 2017; Rudinger et al.,
In this work, we analyzed how epistemic mark- ers impact model behavior. We find a drop in ac- curacy when naturalistic expressions of certainty (i.e., strengtheners and factive verbs) are present and trace this effect back to how expressions of certainty and uncertainty are used in pretraining datasets. As the community expands the capabili- ties of LMs to general language that is more natu- ral, it critical we prepare for the opportunity and harms that may arise from naturalistic expressions of uncertainty and certainty.
2018; Jiang and de Marneffe, 2021, inter alia) as well as bias (Pryzant et al., 2020; Patel and Pavlick, 2021) and specific devices like hedges (Prokofieva and Hirschberg. 2014; Raphalen et al., 2022), and modality (Pyatkin et al., 2021).
work bridge between these two areas of speaker commitment and natural lan- guage generation, by telling us how models inter- pret speaker commitment through expressions of certainty and uncertainty.
We
Limitations
As with many language model work, some key limitations of our work include scaling to other models, increasing the variety of datasets, and ex- perimenting with multi-shot prompting. The work and results we presented are robustly tested but if given additional resources (both in compute and time), the scalability of these results would be of value the greater community.
9 Discussion and Conclusion
Here, discuss number of recommendations and future work for the greater NLP community.
Can Models Generate Expressions of Uncer- tainty? Having studied the impact of uncertainty as part of the prompt, a natural follow-up ques- tion is: how does model performance change when models learn to emit their own expressions of un- certainty? In preliminary experiments, find it is challenging calibrate models generate epis- temic markers. The same methods which work for numerical calibration do not transfer well to verbal calibration (Lin et al., 2022). However, we find some evidence that expressions of uncertainty are slightly better calibrated when compared with expressions of certainty. See A.1.
Multi-shot prompting would dramatically in- crease the exploration space of epistemic markers, but this is a highly realistic scenario that should be explored in future work. Similarly, long-form and dialogue generation are both beyond the scope of this project but would further build our under- standing how models interpret expressions of un- certainty.
Expressions of uncertainty also vary signifi- cantly across cultures and contexts and our study is limited by only studying how hedges and boosters exist in the English language. Syntatic, idomatic, and pragamtic differences in hedges could be inter- esting to study in follow-up work.
Navigating Idiomatic Language Humans use language that contains expressions of certainty when they are, fact, not certain, and models ap- pear to be mimicking this behavior. However, our QA models are still unable to recognize the mean- ing of those markers in our QA setup, raising con- cerns for how they would respond in downstream applications. Future work must seek to enable LMs accurately interpret when expressions meant idiomatically versus literally.
Ethics Statement
Our work complies with ACL's ethical standard. We hope our work inspires future researchers continue investigate epistemic markers in lan- guage modeling. However, we have two critical ethical considerations when it comes future work regarding generating expressions of uncertainty. First, we are concerned about the use of un- verified attributions in language generation as the use of evidentials could appear to be very vincing to downstream users meanwhile provid- ing little guarantee the accuracy of the state- ments. Second, when it comes to generating epis- temic markers, teaching models to only emit ex- pressions of uncertainty when they are unsure,
Verified Attribution We encourage the commu- nity to explore how to integrate attributions of information in a verified manner. Some of the perf-performing templates from Section 4 include phrases like "Wikipedia says... however these were falsely injected attributions. As move to- wards generating expressions of uncertainty, it is critical for researchers to be cautious when generat- ing attributions at the risk of providing downstream users with a false, yet highly believable attribution.

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rather than when they are sure, could be a safer de- sign choice for human-computer interactions. Prior work has shown cases where Al-assisted decision- making performed worse than human decision- making alone, suggesting an over-reliance on AI, even when systems are wrong (Jacobs et al., 2021; Bussone et al., 2015). Teaching models to emit expressions of certainty could further exacerbate these challenges.
Judith Degen, Andreas Trotzke, Gregory Scontras, Eva Wittenberg, and Noah D Goodman. 2019. Definitely, maybe: A new experimental paradigm for investi- gating the pragmatics of evidential devices across languages. Journal of Pragmatics, 140:33-48.
Shrey Desai and Greg Durrett. 2020. Calibration of pre-trained transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Lan- guage Processing (EMNLP), pages 295-302, Online. Association for Computational Linguistics.
Acknowledgements
Leo Gao, Stella Biderman, Sid Black, Laurence Gold- ing, Travis Hoppe, Charles Foster, Jason Phang, Ho- race He, Anish Thite. Noa Nabeshima, et al. 2020. The pile: An 800gh dataset of diverse text for lan- guage modeling. arXiv preprint arXiv:2101.0000
Thank you so much to Tolúlopé Ogúnrêmí, Yi- wei Luo, Myra Cheng, Rishi Bommasani, and Omar Shaikh for their helpful feedback and advice! Thank you to Center for Research Foundation Models (CRFM) for their generous support, and to the National Science Foundation for funding via award IIS-2128145. Tatsunori Hashimoto and Dan Jurafsky were supported by a gift from Open Philanthropy. Kaitlyn Zhou is supported by the Stanford Graduate Fellowship.
Adam Gleave and Geoffrey Irving. 2022. Uncer- tainty estimation for language reward models. arXiv preprint arXiv:2203.07472.
Hila Gonen. Srini Iyer, Terra Blevins, Noah A Smith, and Luke Zettlemoyer. 2022. Demystifying prompts in language models via perplexity estimation. arXiv preprint arXiv:2212.04003.
Ken Hyland. 1998. Boosting, hedging and the negotia- tion of academic knowledge. Text & Talk, 18(3)339- 382.
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A Appendix
of probability-on-gok and random ordering. Fi- nally, we experiment with variety of thresholds (0.3, 0.5, 0.7, 0.9) for determining when expres- sions of (un)certainty should be inserted into the example. These perturbations are done on a small scale to help identify the best hyper-parametery (threshold, placement, and ordering) to use.
Additional Details
A.1
When LMs Emit Their Own Uncertainty
Here, study how model performance changes based on in-context learning examples. Specif- ically, we follow Lin al. (2022)'s 's method in few-shot learning with 50 samples which has been shown to be nearly as effective as fine-tuning on datasets that are magnitudes larger. To ensure that our in-context learning dataset covers a range of confidence levels, our dataset contains 48 samples whose probability-on--oll is uniformly distributed (in buckets of 10) between 0 and 100.
We find that varying the threshold across does not drastically change accuracy (with all methods attaining ~ 83% in accuracy), although the thresh- old does significantly impact the balance of the training datasets. Similarly, we find limited differ- ences in the ordering of the samples. With these results, choose threshold of 0.5 (creating balanced dataset) and random ordering (simplest setting) as our
A.1.1 Experiment Details
for our remain- ing scenarios and analysis, which we test on 100 TriviaQA questions.
To study how LMs respond when emitting their own uncertainty, we follow Lin et al. (2022)'s setup but modify it for the strengtheners and weakeners, which are inherently non-numerical (Figure 1). In our setting, instead of teaching model to output a percentage confidence, we teach to output a strengthener when the confidence is above thresh- old and nothing otherwise. 8 Conversely, when we study weakeners, we teach it to output a weakener when the probability is below a threshold and noth- ing otherwise.
A.1.3 Results
Gains in Model Calibration When Learning Un- certainty Overall, GPT3 has a limited ability to learn naturalistic expressions of uncertainty in a calibrated manner. We measured calibration based on whether models successfully emit the expres- sions of uncertainty when the probability of the top token above or below our training threshold. In our setup, when learning to emit certainty, answers with probability-on-gole of greater than 0.5 had a strengthener and answers with less than 0.5 had nothing. Therefore, in its generation when the prob- ability on the top token is greater than 0.5, we'd expect the model to also generate strengthener and vice versa for weakeners. We measure whether the model successfully generates strengtheners and weakeners through the F1 score, and find that the template with the highest macro-FI score for un- certainty templates to be 0.56 compared to 0.53 for certainty templates." !! This close to the random guessing baseline on this test set which results in an Fl score of 0.45.
As an example, consider the question "What is the capital of France". We record the LM's proba- bility over Paris (the coobabbiltyy-n-gollle and ap- pend "I'm n sure" to the in-context example if the model's confidence was above 0.5. We repeat this for all the in-context examples to obtain our in- context learning training set.
A.1.2 Prompting Perturbations
Recent work has shown the drastic differences that appear based on simple changes to prompting setup (Suzgun et al., 2022; Lu et al., 2022). We design our in-context learning samples with various per- turbations ensure robustness in results.
We select high performing weakeners and strengtheners from Section and experiment with appending expressions of uncertainty after the an- swer (e.g., "Paris. I think") (Table 12).9 We then use three different sample orderings to perturb our learning samples: ascending and descending order
To illustrate the difference in calibration between uncertainty and certainty, we can look at the aver- age accuracy when a model emits an expression or not. When learning to express weakeners, the gen- eration of a weakener results in an accuracy of 74% but this increases to 83% when the model doesn't
"We choose to emit nothing rather than emit an expression of uncertainty, as we wish to isolate the effect each linguistic expression of uncertainty.
"In the ascending and descending orders, all samples beginning or all the samples at the will include expressions of (un)certainty.
'Here, we exclude expressions with attrihution shields for concerns of false attribution, more on this in the discussion.
"Average F1 scores being .52 average for uncertainty and .49 average of certainty

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difference of probing for the answer before (e.g.. "I think it's Paris.") after (c.g.. "Paris. think.") an expression of uncertainty can have significant difference in performance. fer to these places as prefixes (before) and suffixes (after). We find that when appending expressions of uncertainty icantly worse for accuracy (63% vs 80%). This is also correlated with probability-on-gold being lower in prefixed templates (40% vs 67%). An explanation for this might be that the probability of generating the correct answer will be lower if generated after phrase like think it's..." rather than just generated immediately after the question. Our work suggests that ordering effects may be important when addressing accuracy-cal accurcy-calibration trade-offs in LMs and that there accuracy gains when prompting the model to respond with answers as soon as possible.
Entropy
Emitting
Not Emitting
Uncertainty
0.699*
0.522
our tables, we re-
Certainty
0,461
0.617#
Control
N/A
0.541
Accuracy
Emitting
Not Emitting
prefix, the generation is signif-
Uncertainty 0.738
0.829*
Certainty 0.799
0.789
Control
N/A
0.78
Table 3: Entropy is higher when uncertainty being expressed and also higher when certainty not pressed. Accuracy is also higher when uncertainty is not expressed but accuracy not significant higher when certainty is expressed (an indication poor calibration). *Significantly higher value calculated using two-sample t-test, p 0.05.
generate a weakener. This is the intended behavior, with the model hedging answers is more likely to get incorrect. However, when teaching the model to emit strengtheners, the generation of strengtheners does not lead to significant increase in accuracy (79% with without an emission of strengtheners). This means that when the model emits certainty, the answer is not more likely be correct, creating concerning issue for linguistic calibration (Table 3).
Template
Certainty
Prob
Top
Prefix
Uncertain
0.388
0.592
Certain
0.407
0.674
Suffix
Uncertain
0.674
0.800
Certain
0.673
0.792
Control
N/A
0.673
0.780
Table 4: Average probability generated token and top accuracy across prefix and suffix templates.
Modeling Changes in Entropy Despite low model calibration when emitting expressions of cer- tainty and uncertainty, find that the underlying entropy of the probability distribution of the gen- erated answer is well-calibrated to the expressions uncertainty. Analyzing the top five predictions for each token, we find that when teaching mod- els weakeners, the entropy of the distribution of potential generations is higher when weakener is emitted and lower when it is not. The inverse is true when teaching models strengtheners, entropy is lower when strengtheners are emitted and higher when it is not. Although the calibration scores are not strong for either uncertainty or certainty, we see promising behaviors in the entropy of the model's top generations. When emitting weakeners, the model places more consideration on alternative an- swers and less when emitting strengtheners.
A.2
Perplexity Accuracy
davinci
0.35
0.30
Accuracy "ivvaa
0.25
0.15
0.10
102
Perplexity
Figure 6: Correlation between the perplexity (GPT3 davinci) and the accuracy of an expression of uncertainty questions from TriviaQA (Pearson's p -0033..
Sensitivity Placement of Temppate Finally, we test how model performance differs based on the placement of the templates. The simple design

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Comppee rol de: Thn mobells.." "Algeey Fi.." "Dhit 1A6/9TS. Pleas do nntide vomple
Yos whilicc unu oa hert.
uoud
ahaacs vouduse serae...
vnstyca vouduignee...
Figure 7: Screenshot of the Crowdsourced Example
A.3
Performance of Additional Linguistic Features
Accuracy Pausibilty Shiel
bilty Not 2 byciiitt
2.5
0.2
0.0
Countrya
hatura
Darscets
Acturaty by Source
Sourre
No Source
0.6
0.4
0.2
CountryCA
joosöndy
Notura
Ducasets
Accuracy by Personal Fronouns
Pesonill Foreus
Wrt Personal Proncun
2.8
3.4
0.2
CountryAA
jeaberdy Dxpasets
Relure QA
P-leGA
Figure 8: The use of plausibility shields, sources, and personal pronouns are mixed, without significant con- sistent improvements drops in accuracy. 95% CI calculated using bootstrap resampling.
A.4 Additional Tables and Results

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# per thousand posts
per million words
# per thousand posts
# per million words
Expressions
uncertainty
instances
# instances
Questions
Answers
think it could be it might be maybe it's it should be
hedge
1,106,442
37.5
162.2
1,536,543
52.0
302.7
hedge
84,239
2.9
12.3
143,670
4.1
28.3
hedge
70,606
2.4
10.3
170,803
4.9
33.6
hedge
21,803
0.7
3.2
17,233
0.5
3.4
hedge
233,686
7.9
34.3
346,290
10.0
68.2
Total
1,516,776
51.4
222.3
2,214,539
63.9
436.2
know
booster
1,672,756
56.6
245.2
350,241
10.1
69.0
i'm certain
booster
5,975
0.2
0.9
2,758
0.1
0.5
am certain
booster
4,638
0.1
0.7
1,607
0.0
0.3
i' m sure
booster
119,224
4.0
17.5
76,009
2.2
15.0
am sure
booster
52,089
1.8
7.6
22,983
0.7
4.5
it must be booster
52,976
1.8
7.8
72,724
2.1
14.3
evidently it's
booster
33
0.0
0.0
52
0.0
0.0
Total
1,907,691
64.58
279.6
526,374
15.2
103.7
Table 5: Counts of expressions of certainty and uncertainty in the Stack Exchange section of The Pile.
CountryOA
TritaGA
54
1000
0,50
699
3Co
300
3r5
0.55
20.0
b0od
0.02
0k exaroc 83
4.2
0% chanesss Ita
000
préeti its
0% ky to
6.00
20
6% coridertita
300
83
2% confidotits
000
Tam 0% connroed Rt sura
an 24 convinses to
100
0-2
23
NaturalDA
Joopardy Quistiors
10.0
100
20
ILLE
1000
0.40
100
200
627
SE.
100
Act
38y
U00
10
0.20
210
SED charcs it
0000
000
360
exnnc
0.5
ism 69 condoertine
B000
d' coniteet
owrain
the
U/ convreed
20
corficence
2000
2.2
20
100
23
60
Figure 9: Variation in probability-on -oggld across numerical uncertainties. Note the consistent drop in accuracy between 90% and 100% uncertainty and the increase in accuracy between 0% and 10% uncertainty.

---

Template
Strengtheners
Shield
Evidential Marker
Factive Verb
Source
1P
Apparently it's
Weakener
None
Evidential
Not Factive
No Source
No
Rumor says it's Allegedly it's I was told it's I've heard it's
Weakener
None
Evidential
Not Factive
No Source
No
Weakener
None
Evidential
Not Factive
No Source
No
Weakener
None
Evidential
Not Factive
No Source
Yes
Weakener
None
Evidential
Not Factive
No Source
Yes
They told me it's Wikipedia suggests it's
Weakener
None
Evidential
Not Factive
No Source
Yes
Weakener
None
Evidential
Not Factive
Source
No
Online says it's
Weakener
None
Evidential
Not Factive
Source
No
The internet says it's Wikipedia claims it's Wikipedia says it's I read on the internet it's I read on Wikipedia it's I read online it's Presumably it's
Weakener
None
Evidential
Not Factive
Source
No
Weakener
None
Evidential
Not Factive
Source
No
Weakener
None
Evidential
Not Factive
Source
No
Weakener
None
Evidential
Not Factive
Source
Yes
Weakener
None
Evidential
Not Factive
Source
Yes
Weakener
None
Evidential Not Evidential
Not Factive
Source
Yes
Weakener
None
Not Factive
No Source
No
To the best of my knowledge it's As far I'm aware it's I vaguely remember it's It could be
Weakener
Plausibility
Evidential
Not Factive
No Source
Yes
Weakener
Plausibility
Evidential
Not Factive
No Souree
Yes
Weakener
Plausibility
Evidential
Not Factive
No Source
Yes
Weakener
Plausibility
Not Evidential
Not Factive
No Source
No
Considering all
Weakener
Plausibility
Not Evidential
Not Factive
No Source
No
the options it's
probably is Maybe it's
Weakener
Plausibility
Not Evidential
Not Factive
No. Source
No
Weakener
Plausibility
Not Evidential
Not Factive
No Source
No
Perhaps it's
Weakener
Plausibility
Not Evidential
Not Factive
No. Source
No
should be
Weakener
Plausibility
Evidential
Not Factive
No Source
No
don't know maybe it's suppose it's
Weakener
Plausibility
Not Evidential
Not Factive
No Source
Yes
Weakener
Plausibility
Evidential
Not Factive
No Source
Yes
would need double check maybe it's wouldn't 't put money on but maybe not an expert maybe think it's
Weakener
Plausibility
Not Evidential
Not Factive
No Source
Yes
Weakener
Plausibility
Not Fvidential
Not Factive
No Source
Yes
Weakener
Plausibility
Evidential Evidential
Factive
No Source
Weakener
Plausibility
Factive
No Source
feel like should be is known that it's The most recent evidence shows it's The rules state it's Two recent studies demonstrate it's
Weakener
Plausibility
Not Evidential
Not Factive
No Source
Yes
Strengthener
None
Evidential
Factive
No Source
Strengthener
None
Evidential
Factive
Source
No
Strengthener
None
Evidential
Factive
Source
No
Strengthener
None
Evidential
Factive
Source
Wikipedia
Strengthener
None
Evidential
Factive
Source
acknowledges it's
Wikipedia confirms it's Our lab has shown it's Evidently it's
Strengthener Strengthener Strengthener
None
Evidential
Factive
Source
No
None
Evidential
Factive
Source
Yes
None
Evidential
Not Factive
No Source
No
According to the latest research it's We can see in the textbook that it's It must be
Strengthener
Evidential
Factive
Source
Strengthener
None
Evidential
Not Factive
Source
Yes
Strengthener
None
Not Evidential
Factive
No. Source
No
We realize it's We understand it's
Strengthener
None
Not Evidential
Factive
No Source
Yes
Strengthener
None
Not Fvidential
Factive
No. Souree
Yos
We know it's
St Strengthener
None
Not Evidential
Factive
No Source
Yes
Undouhtedly it's
Strengthener
None
Not Fvidential
Not Factive
No. Souree
No
With 100% confidence it's I'm certain it's
Strengthener
None
Not Evidential
Not Factive
No Source
No
Strengthener
None
Not Evidential
Not Factive
No Source
Yes
I 100% sure it's It's
Strengthener
None
Not Evidential
Not Factive
No Source
Yes
None
None
Not Evidential
Not Factive
No Source No
Table Full list of expressions of uncertainty coded linguistic features. *Claims neg-factive but in our schema, will just be considered not factive verb. (Saurí and Pustejovsky, 2009) 2009)

---

Template
Type
Top Accuracy
0 Online says it's
Weakener, Boodee
0.660
1 Standard Method
0.625
2 Wikipedia confirms it's
Strengthener, Evidential, Factive, Source
0.600
3 Wikipedia suggests it's The internet says it's Wikipedia claims it's
Weakener, Evidential, Source
0.595
Weakener, Evidential, Source
0.585
Weakener, Evidential, Source
0.575
6 Wikipedia says it's
Weakener, Evidential, Source Strengthener, Evidential, Source, 1P
0.575
7 We can see in the textbook that it's
0,565
8 I would need to double check
Weakener, Plausibility, IP
0.555
but maybe it's
9 Rumor says it it's
Weakener, Evidential
0.550
Table 7: Top 10 Templates For TriviaQA for GPT3 Davinci
Template
Type
Top Accuracy
Standard Method
1.0
read on Wikipedia it's
Weakener, Evidential, Source, 1P
It's
1.0
3 It should be
Weakener, Plausibility
1.0
4 Allegedly it's
Weakener, Evidential,
1.0
5 I'm not an expert but maybe it's
Weakener, Plausibility, 1P
1.0
6 I wouldn't i put money on it but maybe it's
Wcakener, Plausibility, 1P
1.0
7 Presumably it's
Weakener
1.0
read online it's
Weakener, Evidential, Source, 1P
1.0
read on the internet it's
Weakener, Evidential, Source, 1P
1.0
Table 8: Top 10 Templates For CountryQA for GPT3 Davinci
Template
Type
Top 1 Accuracy
0 Standard Method
0,450
It must be
Strengthener, Factive
0.390
2 It's
0.380
3 It could be
Weakener, Plausibility
0.370
4 The internet says it's
Weakener, Evidential, Source Weakener, Evidential, Source Strengthener Strengthener
0.370
Online says it's
0.360
With 100% confidence it's
0.350
7 Undoubtedly it's
0,345
8 Wikipedia says it's
Weakener, Evidential, Source Strengthener, Evidential, Factive, Source
0.345
9 Wikipedia confirms it's
0,325
Table 9: Top Templates Jeopardy GPT3 Davinci

---

Template
Type
Top Accuracy
Wikipedia claims it's
Weakener, Evidential, Source Weakener, Evidential, Source
0.340
Wikipedia says it's
0,335
2 Online says it's
Weakener, Evidential, Source Weakener, Evidential, Source Weakener, Evidential, Source Strengthener, Evidential, Factive, Source
0,310
3 Wikipedia suggests it's
0,305
4 The internet says it's
0.300
5 Wikipedia confirms it's
0.300
read on Wikipedia it's Presumably it's
Weakener, Evidential, Source, 1P Weakener
0,295
0,275
Standard Method
0,275
9 think it's
Weakener, Plausibility, IP
0.270
Table 10: Top 10 Templates for NaturalQA GPT3 Davinei
Template
Type
Top Accuracy
The internet says it's Wikipedia says it's
Weakener, Evidential, Source
0.416
Weakener, Evidential, Source
0,408
2 Online says it's
Weakener, Evidential, Source
0,405
3 Wikipedia suggests it's
Weakener, Evidential, Source
0,404
4 Wikipedia claims it's
Weakener, Evidential, Source
0,400
5 Wikipedia confirms it's
Strengthener, Evidential, Factive, Source
0.397
6 I would need to double check but maybe it's
Weakener, Plausibility, 1P
0.387
I'm not an expert but maybe it's
Weakener, Plausibility, IP Strengthener, Strengthener, Evidential, Source, 1P
0.387
8 I am 100% sure it's
0.385
9 We can see in the textbook that it's
0.382
Table 1I: Top 10 Templates Across All GPT Models and All Datasets
Expression
Suffix
Prefix
Certainty
Undoubtedly.
Undoubtedly it's
Certainty
With 100% confidence.
With 100% confidence it's
Certainty
We know it. Evidently.
We know it's Evidently it's
Certainty
Certainty
It must be.
It must be
Uncertainty
think.
I think it's
Uncertainty
It could be. But I would need to double check. would need to double check but maybe it's suppose it's
It could be
Uncertainty
Uncertainty
suppose.
Uncertainty
But I wouldn't 't put money on it.
wouldn't t put money on it but maybe it's
Table 12: List of Templates Used for Section A.


Reviews:
[{"review_id": "08zi9YoKMB", "reviewer": "Reviewer_JAsR", "summary": "", "questions": "- A: Why did the authors refrain from using approximators in their work (introduced in Section 2)?\n- B: Will you make the two new datasets available to the community? Will you report which 200 respectively 53 QA pairs you chose from the four datasets?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* One line summary: We conducted new experiments across all four datasets on six models, and our results reproduced nearly exactly, further confirming our findings. \n* We thank the reviewer and agree that analyzing the behavior of LLMs with respect to epistemic markers is of great value to the community and has significant practical implications moving forward. We’ve conducted a number of experiments following your comments which have helped us strengthen the rigor and soundness of our findings. Details below.\n###### Process of Building Epistemic Markers\n* While there’s a diverse literature on epistemic markers, there is no single coherent taxonomy in the literature. Therefore, we engaged in a bottom-up process where we gathered a diverse set of templates guided by the linguistic literature and used the literature to develop our taxonomy and classify our templates. To gather the templates, the authors relied on linguistic literature like Prince et al. 1982, Amazon Mechanical Turk, and brainstorming among the authors. Next, we drew on additional linguistic literature to augment this list, adding dimensions such as factive verbs (Kiparsky and Kiparsky, 1970), evidentials (a broader category of attributions) (Aikhenvald, 2004, 2020), and authoritative sources. We then integrated this literature to develop a coherent taxonomy and classify each of our templates. \n* We aim to use the extra page in the final version of the paper to include these details. Thank you for highlighting this!\n###### Description of crowdsourcing\n* We will move the description into the main text for the final draft!\n###### Details of Model setup\n* The model set up is reported in detail in the appendix (section A.1) and we will move this into the main text for the final draft. We aim to release our code and artifacts with the final version of the paper to allow for reproducibility. For additional transparency, given the frequent changes in models, we will also provide timestamps of when the models were prompted. We used the davinci model from Feb 2023 on all four datasets. Experiments on ada, babbage, curie, text-davinci, and GPT4 models on the TriviaQA dataset were all conducted in June of 2023. Experiments on ada, babbage, curie, text-davinci, and GPT4 models on the CountryQA, NaturalQA, and Jeopardy dataset were conducted last week, August 2023.\n###### Evaluation design\n* We’ve used the time during the response period to evaluate all six models across the four datasets. We present the aggregated results across the four models below and will include detailed results for each model and dataset in the appendix. The results replicated almost exactly with the four datasets together, highlighting the potential harm of overconfidence and factive verbs as well as the gains of evidential markers.\n\n|          |Ada    |Babbage |Curie   |Davinci |text-davinci-003|GPT4    |\n|-----------------|-------|--------|--------|--------|----------------|--------|\n|Booster          |0.091  |0.257   |0.313|0.392   |0.589           |0.793   |\n|Hedge            |0.079  |0.272   |0.333***|0.468***|0.642***        |0.822***|\n|Evidentials      |0.087**|0.281***|0.347***|0.449*  |0.640***        |0.820***|\n|Not Evidentials  |0.080  |0.250   |0.301   |0.433   |0.601           |0.799   |\n|Factive Verbs    |0.078  |0.237   |0.293   |0.347   |0.555           |0.771   |\n|Not Factive Verbs|0.085* |0.276***|0.336***|0.468***|0.641***        |0.821***|\nt-test p-values, * < 0.05, ** < 0.01, *** < 0.001\n\n###### Conclusiveness of the results from 4.2\n* Using our results with six models across all four datasets, we aggregated the results by template and using t-tests of independence, we identified six templates which perform significantly better (p-value < 0.05) than the standard method. We agree that stronger claims about this phenomenon would require further work, and we hope that the community will continue to investigate how epistemic markers can improve model accuracies.\n###### Numerical test set was downsampled\n* The test sets were downsampled because introducing numerical prompting significantly increases the prompting budget we need. For each template, we  prompted the model seven times (once for each numerical interval), given our constraints, we felt it necessary to downsample the test sets.\n######  “twice as often” should obviously be replaced by “half as often”.\n* Thank you for this! It should be “half as often”.\n###### Refrain from using approximators\n* Great question! Approximators are great for describing items/things that are continuous. However, given that we are mainly working with trivia questions which have discrete answers (such as the capital of countries) we did not find approximators to be as relevant. We will include this justification in our final version. We would love to see work on this in the future!\n###### Making datasets available:\n* We can definitely make the CountryQA dataset available to the community! The second dataset, Jeopardy, is not ours but is linked in our paper and we would encourage the community to use the link and cite it appropriately.\nWe’ve included the list of templates in the appendix but can include it in our GitHub Repo as well.\n###### Typos:\nThank you for all these notes! We really appreciate the time you put into this review and will make sure to incorporate the feedback into our final version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Analyzing the behavior of LLMs with respect to specific linguistic phenomena is of great interest and value to the community in order to better understand the performance of these models and discover potential pitfalls for practical application. This work contributes to this by focusing on markers of certainty and uncertainty.\n- The paper is well written and easy to follow. The motivation of the paper and included experiments is very clear.\n- The contrasting findings of the study with respect to the two intuitive working hypotheses are intriguing.\n- Particularly precious in this work is the detailed analysis of the why in Sections 5 to 7, which reveals, for example, unfavorable properties of the pre-training dataset that may provoke the unexpected effect.", "reasons_to_reject": "- Section 2.1: The process of building the list of epistemic markers is very unclear. It should be explained in detail which input came from the literature, which came from the authors and with which substantiation. Furthermore, the description of crowd sourcing some of the markers should be motivated and included in the main body of the paper rather than in the appendix. [convincingly addressed in the rebuttal]\n- The model setup should be reported in detail, such as which pre-trained models are exactly used (e.g., add a link to the model if they are from huggingface).\n- The evaluation design was seriously restricted in that models other than GPT-3 were evaluated on only one of four datasets. Since the datasets are small, this limitation is not understandable and reduces the significance of the results. [convincingly addressed in the rebuttal]\n- Section 4.2: The conclusiveness of the results is questionable, as they appear to be single observations. Therefore, they need to be confirmed, for example, with a regression analysis.\n\nMinor issues:\n- line 449-452: It is not clear why the test set was downsampled. Please explain your decision. [justification provided in the rebuttal]", "questions_to_authors": "- A: Why did the authors refrain from using approximators in their work (introduced in Section 2)?\n- B: Will you make the two new datasets available to the community? Will you report which 200 respectively 53 QA pairs you chose from the four datasets?", "paper_topic_and_main_contributions": "This paper examines how LLMs respond to epistemic markers of certainty and uncertainty.  To this end, the authors develop a list of 50 different modifiers based on linguistic theory, which they insert into QA prompts. They explore two main hypotheses: 1. LLMs are robust to these modifiers. 2. LLMSs respond to the modifiers in that the certainty of the modifiers increases accuracy and vice versa. The experiments contradict both hypotheses. 1. LLMs (GPT-3 in the focus of this work) are not robust to the markers, as indicated by significant changes in accuracy. 2. LLMs respond to the markers in the opposite way as hypothesized.  The authors then analyze in depth why LLMs behave as encountered by looking at the correlation with prompt perplexity, whether the underlying probability distribution is altered by the markers, and the extent to which certainty and uncertainty are present in questions and answers included in QA pre-training datasets. Moreover, they add degrees of uncertainty in the form of percentages to specifically analyze the impact of the spectrum on LLM performance. This extended analysis provides valuable insights into the behavior of LLMs with respect to epistemic markers of certainty and uncertainty.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Location of \"Related Work\": There is a very early reference to this in Section 2, so it seems much more logical to put the \"Related Work\" section up front. (i.e., Section 2 \"Related Work\", Section 3 \"Expressions of Certainty and Uncertainty: Linguistic Background\").\n- Figure 2: Include all groups of markers.\n- In several sections there is only one subsection. Usually one has none or several. You may think about modifying this accordingly.\n- There is a repeated use of reference to “Appendix”. This is not very helpful as the appendix is extensive. Please include the exact section in these cases.\n- The location of Figure 1 is a bit unfortunate as it is referenced pages after. However, I feel that the content of the figure fits well in the introduction, so the reference could be made accordingly earlier.\n- Shift Table 2 into Section 5.2.\n- line 466: “Figure 4”\n- Section 7 title: 100% of what? (certainty?)\n- In general, it would be much easier for the reader if the sections in the appendix were arranged chronologically as they are referenced in the main body.\n- line 395: There is a content error here: “twice as often” should obviously be replaced by “half as often”."}}, {"review_id": "annqmaW4sL", "reviewer": "Reviewer_K4oh", "summary": "", "questions": "1.\tThe paper describes the results across multiple linguistic phenomenon: whether the expression is a strengthener (reflects certainty) or weakener (reflects uncertainty), whether the expression includes an evidential marker (for e.g., “Wikipedia suggests it’s”) and whether the expression is factive, that is, it presupposes the truth of its complement cause (e.g., “I realized”). Is the effect of expression of uncertainty mediated by the presence of evidential markers? From Tables 7-10, it seems that most of the top templates from the weakener class are also evidential in nature. There might be an interesting underlying phenomenon at play here, given that most of the evidential markers of the strengthener type are also factive. Maybe, the difference in the accuracy for strengthener and weakener class is explained away by the other properties itself?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* One line summary: We addressed the reviewer's main concern by qualitatively sampling responses and found that the potential undesirable behavior is very infrequent, ensuring the accuracy and robustness of our scoring. \n* Thank you for your positive review! Thank you for supporting this paper and pointing out that the paper highlights a major flaw in LM behavior. We also thank you and reviewer 1 for both pointing out the thorough analysis that was done to draw out the LM behavior as well as to investigate the potential sources. We hope that our methods, artifacts, and insights will equip the community on best steps forward to addressing this issue. We really appreciate the time you took to engage with our work and for helping us more rigorously present our findings and results.\n###### Scoring process:\n* We manually inspected 1,000 answers to check for this behavior. We found only one instance of this occurring where the first token was “not” and the second token was the correct answer, indicating that this is a very rare behavior. Thank you for pointing this out and helping us be extra rigorous with our results. \n###### Experimental details and consistency across prompts\n* For all models except GPT4, we set temperature to 1 and took the most probable token associated with each generation which should lead to mostly consistent results across multiple prompts. This is in contrast to just taking the generated output which would lead to different generations at each prompting. For GPT4, where the logprobs are currently not available, we set temperature to 0, also ensuring a (mostly) deterministic output.\n###### Questions For The Authors\n* This is a great and keen observation! Given our current set of templates, we don't think we have the statistical power to fully answer how hedges and boosters would perform without the influence of evidentials and factives. We have a limited number of booster templates that are non-factive and non-evidential, making it difficult to fully explore this question. Based on some taxonomies, including the ones we cite (Prince et. al. 1982), evidentials are considered to be hedges as the speaker is not fully committing to the statement but rather attributing part of it to another source. It is interesting to think about hedges that don't contain evidentials and boosters that don't contain factives, but we think this exploration requires consideration of other potential confounders which are best suited for future work. One suggestive piece of evidence we can say from section 6 is that in the numerical uncertainty setting, which contained templates without factives and evidentials, we know that absolute certainty performs worse than hedged expressions. \n###### Typos\n* This is very helpful and will take care of these issues for the final version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThe findings in the paper are quite interesting and highlights a major flaw in LM behavior with respect to epistemic markers of uncertainty, that are contrary to expectations.\n2.\tOverall, the paper conducts very thoroughly analysis, not only to draw out the LM behavior, but also to investigate potential sources of this behavior, leading to useful insights into the problem.\n", "reasons_to_reject": "1.\tSome assumptions in the evaluation might be resulting in undesirable scoring. The paper mentions that “while calculating accuracy, we generate 10 tokens and if any of these tokens matches the answer, we’ll count that as a correct generation. This is done to not unfairly disadvantage templates that are prone to generating words prior to emitting the answer (e.g., ‘Allegedly, it’s said to be…’)”. My concern is that this disregards that LM might output something “Allegedly, it’s believed to be … but facts show that it’s”, or something to this extent, in which case the model would be awarded for either answers. It is also likely that when prompted with expressions of uncertainty, the model might generate multiple possible answers (e.g., “I am not sure, but it’s either … or …), again giving model higher points. This might also be a potential reason for higher accuracy when models are prompted with expressions of uncertainty than expressions of certainty. This evaluation needs further validation to ensure that no unintentional behavior is captured here.  \n2.\tMinor issue, but the paper does not mention a lot of details about the experimental setting. Mainly, is the model prompted with each template, question pair only once. Are the trends consistent on multiple prompting? \n", "questions_to_authors": "1.\tThe paper describes the results across multiple linguistic phenomenon: whether the expression is a strengthener (reflects certainty) or weakener (reflects uncertainty), whether the expression includes an evidential marker (for e.g., “Wikipedia suggests it’s”) and whether the expression is factive, that is, it presupposes the truth of its complement cause (e.g., “I realized”). Is the effect of expression of uncertainty mediated by the presence of evidential markers? From Tables 7-10, it seems that most of the top templates from the weakener class are also evidential in nature. There might be an interesting underlying phenomenon at play here, given that most of the evidential markers of the strengthener type are also factive. Maybe, the difference in the accuracy for strengthener and weakener class is explained away by the other properties itself?", "paper_topic_and_main_contributions": "The paper studies how including expression of certainty (such as, “I’m 100% sure it’s”) and expressions of uncertainty (such as, “Maybe it’s”) in the model prompt in question answering setup affect the model’s accuracy. The authors collect a set of naturally occurring expressions of certainty/uncertainty from literature in linguistics and experiment with these expressions in zero-shot prompting setup. The analysis presented in the paper reveals that contrary to expectations, model performance drops on addition of expression of certainty to the prompt. The paper conducts further analysis to identify reason for this behavior and find that in the pre-training data, the expressions of uncertainty are frequently used in answers (possibly to portray politeness). Expressions of certainty appear more frequently in questions (possibly to rule out incorrect answers). They hypothesize this as a possible cause of the model’s unexpected behavior. The main takeaway of the paper is that LLMs might not be calibrated to expressions of uncertainty, possibly drawing questions on the ability of these models to generate expressions of uncertainty that aptly represents the model accuracy. ", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1.\tTypo on line 395: “occur twice as often in answers (104 instances per million words) as in questions (280 instances per million words)”-> “occur twice as often in questions (280 instances per million words) as in answers (104 instances per million words)”.\n2.\tI think that the last part in the abstract is unclear and uninformative in of itself before reading the paper. I would recommend highlighting the main takeaway here: that the analysis of pre-training data reveals that answers more commonly contain markers of uncertainty and so forth, which reflects that the unexpected behavior of the LMs might be based in mimicking of this behavior, instead of truly reflecting epistemic uncertainty.\n3.\tIn terms of presentation, I would also recommend adding the linguistic type of the templates in the Tables 7-10 as it would help reading these tables without having to go back and forth between these and Table 6. \n4.\tWith the addition space in the final version, I would also recommend adding a subset of examples from templates in Table 6 to the main paper to aid readers in understanding the typology better. \n5.\tThe paragraph in lines 114-120 reads “markers related to certainty and uncertainty, both for weakening category membership and weakening speaker commitment … ”. I think you should mention strengthening/weakening here because otherwise it’s confusing how expressions of certainty would lead to either weakening category membership or weakening speaker commitment. \n6.\tLine 464-466 “We evaluate the ECE and find poor values ranging from 0.50 to 0.30, 0 being the best (Figure 4)”: This is confusing because Figure 4 shows accuracy and not ECE. Please make the discussion consistent with the figure. \n"}}, {"review_id": "Q0KVffYGGF", "reviewer": "Reviewer_SvKk", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 2, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "* Thank you for the review and agreeing this is an interesting topic and highlighting our innovative approach!\n###### Range of Epistemic Markers:\n* We cover a range of epistemic markers as covered in the literature and cited in our paper. We wanted to cover a range of common linguistic features related to epistemic markers and had examples related to each broad linguistic category. We think there is a lot of potential for great follow up work in additional epistemic expressions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "This paper explores the influence of epistemic expressions on language models using zero-shot prompting, an innovative approach.\n\n", "reasons_to_reject": "However, the potential of generalization would be questionable given the limited range of epistemic expressions under examination.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper investigates the impact of epistemic expressions, including certainty, uncertainty, and evidentiality, on language models. It achieves the goal through an innovative approach, employing zero-shot prompting and infusing verbal and numerical markers into trivia question prompts.\nThis is an interesting topic because it examines how specific linguistic issues would affect language models.\n", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper examines how LLMs respond to epistemic markers of certainty and uncertainty and develops a wide range of different modifiers based on linguistic theory, which are inserted into QA prompts. The authors conduct experiments which turn out to contradict the hypotheses/expectations (i.e., LLMs are robust to the modifiers and the certainty of the modifiers increases accuracy and vice versa). The reviewers find this paper very intriguing to read and that analyzing the behavior of LLMs w.r.t. epistemic markers of uncertainty and specific linguistic phenomena in general is valuable to the community and the paper has conducted comprehensive experiments and analysis. Most concerns have been addressed in the rebuttal and the authors promised to incorporate the feedback into the revision. Overall, I'd recommend accepting this paper to the main conference.
Decision: Accept-Main