What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability
Authors: Mario Giulianelli
Joris Baan
Wilker Aziz
Raquel Fernández
Barbara Plank
Venue: EMNLP 2023 Main (2023)
Keywords: uncertainty
NLG
variability
language production
text generation
TL;DR: We propose a statistical framework to assess sequence-level uncertainty in text generators against plausible response variability in humans and perform an empirical study on four NLG tasks.

Abstract: In Natural Language Generation (NLG) tasks, for any input, multiple communicative goals are plausible, and any goal can be put into words, or produced, in multiple ways. We characterise the extent to which human production varies lexically, syntactically, and semantically across four NLG tasks, connecting human production variability to aleatoric or data uncertainty. We then inspect the space of output strings shaped by a generation system's predicted probability distribution and decoding algorithm to probe its uncertainty. For each test input, we measure the generator's calibration to human production variability. Following this instance-level approach, we analyse NLG models and decoding strategies, demonstrating that probing a generator with multiple samples and, when possible, multiple references, provides the level of detail necessary to gain understanding of a model's representation of uncertainty.

What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability
Mario Giulianelli Joris Baan So.
Wilker Aziz Raquel Fernández de Barbara Plank AOR
A University of Amsterdam ITU Copenhagen MCML Munich @ LMU Munich
{m. giwllaneeliliaa baan, raquel. fernandez, w.a.zzevvaa nl, b. plankēlmu de
Abstract
Dialogue context
Semanticc veriabilly
very
you light?
Okay, But our baby has fallen asleop.
Natural Language Generation (NLG) tasks, for any input, multiple communicative goals are plausible, and any goal can be put into words, or produced, in multiple ways. We characterise the extent to which human production varies lexically, syntactically, and semantically across four NLG tasks, connecting human production variability to aleatoric or data uncertainty. We then inspect space of output strings shaped by generation system's 's predicted probability distribution and decoding algorithm to probe its uncertainty. For each test input, we measure the generator's calibration to human production variability. Following this instance- level ap- proach, analyse NLG models and decoding strategies, demonstrating that probing genera- tor with multiple samples and, when possible, multiple references, provides the level of detail necessary to gain understanding of a model's representation uncertainty.'
Then, the lamp, please.
Dut where's the switch?
Humans
Don't know where switch is?
Switch on the left side the lamp.
Dul 0.33 0..0
Just press the second switch on the bound.
now you know where switch is. I will light up the turch, so you can find the swilch and turn on the lamp.
Lamp upon study
DialoGPT- nucleus p p09
You don't have one.
IIm sorry.
Where's the button?
On cheat
top.
Well, you'll want turn it on.
Turming on the switch
Tum
have try figure
Figure I: Production variability observed in 5 human responses 10 responses generated by DialoGPT.
The graph presents the distribution of pairwise cosine distances: generated responses exhibit higher semantic variability than human responses. The generator's 's semantic uncertainty is too high in this dialogue context.
or a yes-no question). The second source of variability is the fact that even when context and communicative goal are fixed, speakers' linguistic realisations of the communicative goal may vary (Levelt, 1993). Both sources of variability apply to individuals well to populations: if an expert is asked to simplify a complicated sentence multiple times, they may perform different rewrit- ing transformations (e.g., paraphrasing, reordering, or sentence splitting) and produce different texts ManchhM et al., 2021); the same is true if multiple experts are asked to perform a task (Xu et al., 2015). If we to regard Natural Language Generation (NLG) system (or text gen- erator) good model of human production, it should capture the variability observed in humans. Text generators combine two mechanisms: (i) an underlying statistical model model-typically gressive factorisation of the probability of se- quences, with conditional token probabilities pre- dicted by neural network; and (ii) an iterative decoding algorithm that chains samples from next token distributions into a complete production. To-
1 Introduction
Humans display great variability in language production, in particular when the context or the task are open-ended, such as in storytelling or in dialogue. Given story prompt, for example, there are many plausible ways in which different humans (or a single writer, if asked multiple times) may tell the story (Fan et al., 2018). We refer to this phenomenon as production variability. Production variability in humans has two main sources. First, when situated in context, speakers may entertain variable communicative goals (Searle, 1969; Sacks et al., 1974; Austin, 1975), and the number and variety plausible communicative goals depends on the production task (Jokinen, 1996). Transla- tion, for instance, defines the communicative goal almost unequivocally while a dialogue context might allow for wide variety of communicative goals (expressed, e.g., request, assertion,
autore-
"Equal contribution. httpss///ghhbbbo.oomnnnlllllllin

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gether these two mechanisms specify a probabil- ity distribution over sequences of tokens, which can be regarded as a representation of the model's uncertainty about productions for a given genera- tion context (see Baan et al. (2023) for a detailed discussion). In this work, we assess whether this representation of uncertainty is in compliance with production variability exhibited by a population of humans-which in turn, argue, be re- garded as an expression of aleatoric uncertainty, ie., irreducible uncertainty due the stochastic na- ture of the data generating process (Der Kiureghian and Ditlevsen, 2009; Hüllermeier and Waegeman, 2021). In other words, we compare the distribution over productions of a text generator against the dis- tribution over the productions of population of human speakers, given the same context (Figure 1).
selection of models and tasks, our work has im- portant implications for NLG evaluation and data collection. Multiple samples and, when possible, multiple references, should be used to assess the statistical fit text generators. Our approach, com- plementary to other types of automatic evaluation, makes model assessments particularly insightful and trustworthy because it does not judge a model only by a single output but also, intuitively, by what could have generated and it does so for each individual input in the test set. We therefore hope our framework will be used by the community as an evaluation criterion for NLG systems, especially to assess them in more open-ended tasks.
Related Work
Automatic approaches the evaluation of NLG systems are of high practical importance: they al- low for model selection at scale and power quality- aware decoding algorithms (Borgeaud and Emer- son, 2020; Eikema and Aziz, 2020; Fernandes et al., 2022; Suzgun et al., 2022). In spite of their known limitations (Gehrmann et al., 2022), they are nec- essary complement to human evaluation (Belz and Reiter, 2006; van der Lee al., 2021).
Quantifying the closeness in distribution between a text generator and a human population is difficult: we only have an iterative view into the generator's distribution; the 'human distribution' is an implicit or even hypothetical object; and in both cases, the sample space is large even unbounded. We can, however, compare these two objects via the samples they produce and assess their statistical distance- which what we propose here. For each individual generation context, we compare scalar properties of generations (through repeated model sampling) and human productions (using multi- reference NLG datasets). In particular, we probe for lexical, syntactic, and semantic distance be- tween productions, thus allowing for quantitative and interpretable assessment of uncertainty.
Reference-based evaluation. The most com- way of automatically evaluating text gener- ators is via metries that estimate the similarity be- tween candidate generations and references, such as BLEU (Papineni et al., 2002), ROUGE (Lin, 2004), COMET (Rei et al., 2020), BLEURT (Sel- lam et al.. 2020), and BertScore (Zhang et al., 2020a). Reference-
metrics are less suited for open-ended tasks such story generation and dialogue, where single reference (or even hand- ful) cannot be representative of the large space of plausible communicative goals and realisations.
We find that the uncertainty of neural text generators is higher than justified by human production variability in open-ended tasks, like story generation and open-domain dialogue; and that it is lower on more constrained tasks, like machine translation and text simplification. Popular decoding algorithms, which bias away from the distribution of the generator's underlying statistical model (e.g., top-k; top-p, or locally typical, rather than ancestral sampling), have a lim- ited impact on the generator's ability to faithfully represent human variability. We complement our quantitative assessments with a detailed analysis of individual generation contexts, which sheds light whether a generator has robustly learned to reproduce degrees and aspects of human variability plausible for the communicative task.
Reference-free evaluation. A popular, reference- free alternative is train evaluation models that discriminate human from model output (e.g.. Bruni and Fernánde7, 2017; Gehrmann et al., 2019; Hashimoto et al., 2019), score the ap- propriateness of ouput- pairs (e.g., Sinha et al., 2020; Fomicheva et al., 2020), or model human judgements directly (e.g., Lowe et al., 2017; De Mattei al., 2021: Rei et al., 2021). Neural language models themselves have been proposed as evaluators (e.g., Yuan et al., 2021; Deng et al., 2021) and used to assess generations along interpretable evaluation dimensions (Zhong
Beyond the experimental results obtained on our

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al., 2022), yet they have been criticised for being biased (toward models similar to the evaluator) and thus limited their ability to evaluate generated text (Deutsch et al., 2022).
in translation, a story prompt in story generation. or more generally the input to a language process. In turn, production is a piece of text y such as a single translation, a story, or more generally the output of a language process.?
Statistical evaluation. Statistical evaluation compares model generations to human productions in distribution through real-valued statistics (e.g.. Zipf's coefficient, type-token ratio, length) as opposed to strings themselves. These statistics are typically compared marginally, at the corpus level (Eikema and Aziz, 2020; Meister and Cotterell, 2021; Pillutla al., 2021; Pimentel et al., 2022), supporting general claims about model performance in relation to humans. More recently, Barkhof and Aziz (2022) and Deng et al. (2022) compared statistics at the instance level, supporting claims about models' performance in relation to humans for individual inputs. In this work, we craft statistics that evaluate generators' uncertainty the instance level against the variability over sequences observed in multi-reference NLG datasets. Although evaluating uncertainty is gaining traction in NLP (e.g.. Desai and Durrett, 2020; Glushkova et al., 2021; Baan et al., 2022), there is relatively little work on sequence-level uncertainty (Ott et al., 2018; Malinin and Gales, 2020; Aina and Linzen, 2021; Kuhn et al., 2022).
3.1 Production Variability
For any language process, production variability is fully characterised by conditional probability distribution Py|X=r representing uncertainty about the output Y given input X = I. Intuitively, the uniform distribution maximises production vari- ability and the Dirac delta (one-hot) distribution minimises it. Analysing this distribution is difficult. Notably, for human language processes, we do not have an explicit representation of Py|X=z- This prevents a direct comparison through measures of statistical divergence, or summaries like entropy. Through data collection we can, however, draw conditional samples from the human language pro- cess (i.e., gather references given context). On the other hand, for NLG models, do have an algo- rithmic representation of py|X=>> which is usually sufficient to enable sampling, but the unbounded sample space and lack of conditional independence assumptions make statistical divergence and sum- maries like entropy intractable.
Instead, propose to analyse language processes through their samples. This in turn introduces other difficulties, as text is a high- dimensional, structured, non-numerical data type. For tractable analysis, we exploit a set of real-valued and interpretable statistics, or production probes, to re-express a language process distribution in terms of how, given input, its outputs relate to outputs of another language process. When both processes are independent humans performing task, we obtain a sense of how plausible human productions relate (or vary with respect) to other plausible human productions. along a linguistically interpretable dimension. When we swap one or both processes for an NLG model, we obtain tools to analyse how model gener- ations relate to plausible human productions, thus assessing model's representation of uncertainty against the variability observed in humans.
Diversity in NLG. Our analysis is related to NLG studies output diversity. Some have evaluated diversity induced by different models and NLG decoding strategies-yet do not use human levels of variability as target (Wiher et al., 2022)-or have used human judgements to evaluate diversity metrics themselves (Tevet and Berant, 2021; Stasaski and Hearst, 2022). Others objective
have developed diversity-enhancii functions (Li et al., 2016) and decoding algorithms (Vijayakumar et al., 2018: Shu et al., 2019; Weir al., 2020; Meister al., 2021). In study, where the aim is evaluate the uncertainty of NLG systems, we focus on unbiased sampling and the most widely used decoding algorithms.
3 Probing Language Processes for Production Variability
We interpret language production, by humans or
Notation. Random variables are denoted by uppercase let- ters (e.g.. Y). outcomes are lowercased (e.g., y), and pyJX= denotes the probability distribution
NLG systems, as captured by probability distri- bution over natural language strings (productions), a random variable Y, given a linguistic context
given X =z. 'In Appendix A, we discuss issues with entropy in more detail how it is both difficult to estimate and interpret for text generators.
X = x. The context x can be a source sentence

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Resden Datighh
Brsticiio Due Dutt
Dae Oue chatage
0.25
638
(a) Lexical variability
(b) Syntactic variability
(c) Semantic variability
Figure Human production variability across four NI.G tasks. The values on the horizontal axis are single samples of lexical (unigram), syntactic (POS bigram), semantic (cosine) distance between two randomly sampled productions for each input (see Section 3). Re-sampling productions results in nearly identical marginal distributions. Probability mass on the right side signals high distance and thus high variability, and vice versa.
Specifically, given a context x. two language processes with distributions PýJx=r and Py|X=== and a choice of distance metric k(..-) E R, our probe for production variability real random variable k Y). This random variable captures the joint distribution of distance between any two outputs drawn conditionally from the two processes. The distribution of the probe A(Y, Y) is also intractable, but simulation by drawing productions from the processes and assessing the distance metric on sampled pairs, as illustrated in Figure 1.
Experimental Setup
4.1 Data and Models
We experiment with four NLG datasets that con- tain 5+ human references per input instance and for which we expect humans to display different degrees of production variability. For each tasks, we select models that are publicly available, are reasonably sized, have been used previously the task, and are conventionally accepted suitable for it. All datasets are in English; for translation, the target language is German. Table 1 (Appendix C) shows relevant statistics. The reference collection procedure varies across datasets and we discuss how this may impact our analysis the Limita- tions section.
estimate it via
Consider analysing the human process (S 5) through &(Y,Y) when multiple realisations of the output are dissimilar (e.g., given the input 'How is your day?' and outputs 'Fantastic, thank you!" and II asked you first') production variability is high along the dimension captured by
Machine translation. We use 500 sentences from the WMT-14 En-De test set (newstest2014; Bojar et al., 2014), which have been annotated by Ott et al. (2018) with 10 additional reference trans- lations produced by as many human translators. As a generator, we use Helsinki-NLa. Transformer- Align model trained on Opus-MT (Tiedemann and Thottingal, 2020).
3.2 Production Probes
We instantiate our production probes with three distance functions. They return values from to 1. We hope that future work will experiment with alternative probes that may capture other linguistic extra-linguistic levels of analysis.
Text simplification. We use the 2,000 instances of the ASSET validation set (Alva-Manchego al., 2020). For each source sentence, originally from the TurkCorpus (Xu et al., 2016), ASSET includes 10 additional simplifications by as many crowdsource annotators. On this dataset, we test F5an-T5-large (Chung et al.. 2022), an instruction- finetuned version of the T5 language model (Raffel et al., 2020), which we further finetune the ASSET training set.
Lexical: The fraction of distinet n-grams in two strings, with n [1, 2,3] (i.e., number of non- matching n-gram occurrences divided by the total number of n-grams in both strings).
Syntactic: Analogous to lexical distance but
part-of-speech tag n-grams.*
Semantic: The cosine distance between the sentence embeddings of two strings (Reimers and Gurevych, 2019).5
'From spaCy, en_core ccoe_web_mmt (Honnibal et al., 2020). "sentence- dtdp:////lllssstllrbrrrrra
Forr text simplification, we did not find any available trained model, so we used a versatile model like F5a

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Storytelling (story generation). We use the 759 instances from the WritingPrompts test set (Fan et al., 2018) for which at least human references are available. Prompts and stories are originally scraped from r/Writiiggromptss a Reddit forum of stories written by online users in response to story prompts designed by other users. The number of stories available per prompt (9.56 + 7.67) varies from 5 to 92. We GPT2-large (Radford et al., 2018) finetuned on the WritingPrompts training set.
Translation and text simplification. Humans show low production variability in these two tasks, While translations of given source sentence are more lexically and semantically varied, simplifica- tions exhibit a higher degree of syntactic variability, probably as result of the instructions used during data collection (writers were asked to use varying rewriting transformations). Overall, low levels of variability to be expected as, in both tasks, con- tent preservation is part of the communicative goal.
Story generation. Variability in story genera- tion is strongly dependent on the probe. It is low at the syntactic level-close to translation and simplification while lexical and semantic probes place this task closer to open-domait dialogue. Sto- ries generated from given prompt may vary lot in content, but basic syntactic structures and lexi- cal material are shared. Although this task can be a priori perceived least as topen-ended as dia- logue, lower levels of variability may result from contextual factors specific to the Writing dataset that we are not explicitly modelling, such as writers reading stories contributed by other users.
Open-domain dialogue. We use the develop- ment set of DailyDialog++ (Sai et al., 2020), which contains 5 additional references for 1,028 conversations from the DailyDialog corpus (Li et al., 2017). The dialogues are short (less than 8 turns) and cover broad list of topics; for each dialogue, 2-3 annotators were asked generate 1-3 alternative responses.' For this task, we use the pre- trained DialoGPT-medium (Zhang et al., 2020b).
4.2 Decoding algorithms
We experiment with five decoding algorithms: un- biased (ancestral or forward) sampling (Bishop, 2006; Koller and Friedman, 2009), temperature scaling, top-k sampling (Fan et al., 2018), nucleus sampling (Holtzman et al., 2019), and locally typ- ical sampling (Meister et al., 2023). For all de- coding algorithms, we set the maximum sequence length to 100 (cf. Table 1, Appendix C).
Open-domain dialogue. We observe the highest production variability in this task across all probes. Many output pairs are lexically and syntactically completely dissimilar, as indicated by the right- most bin in Figures 2a and 2b. Lexical variability is even more extreme when looking at bigrams and trigrams (Figure 7 in Appendix D) suggesting that while responses rarely share words or phrases, they still sometimes convey similar meaning (Figure 2c). Overall, the fact that dialogue appears to be the most open-ended task can be explained by the wide variety of communicative goals that can plausibly follow from a dialogue context and, in part, by the fact that individual annotators produced multiple responses for the DailyDialog+ dataset and thus were able to monitor the diversity of their outputs.
5 Human Production Variability Across NLG Tasks
Consider Pr|x=r the distribution that describes the human language process, and define the following special case for human production variability:
(1)
Estimating this probe by drawing pairs of human productions provides an interpretable view on plau- sible variability-i.. aleatoric uncertainty- along the dimension captured by k. Figure 2 shows (x)x) marginalised over inputs for the four NLG tasks. We use unigram distance for the lexical probe, POS bigram distance for the syntactic probe, and cosine distance for the semantic probe. High distance indicates high variability, and vice versa.
6
Do Neural Text Generators Reproduce Human Production Variability?
Consider, now, second language process: a text generator with distribution p We study this generator's uncertainty about outputs given an input x under two lenses. In $ 6.1, 1, we study how outputs vary with respect to one another, which is analogous to human production variability Hg(()). We refer to this as the generator's seliiviarability:
'The DailyDialog++ annotators are also instructed to avoid short generic responses such as 'Sure" and to write, instead, meaningful responses with at least 8-10 words.
(x) = k(Ý, Y).
(2)

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Lexical varabiltyy
Syntaciic variabilty
Somantic varisbiliyy
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Unigram distance
POS big.aiaammmmmmmmmm dittare
Codta diitance
Figure 3: Distribution of (M,(2) Mn.(s) over instances. Values greater than zero indicate the model overestimates the variability of the task (higher mean pairwise distance); values below indicate variability underestimation.
In $ 6.2, instead, we study how model genera- tions vary with respect to a language process known to be plausible: a human language process PY|X=x' We refer to this as cross-variability:
0.14
0.10
Prebasiity
0.00
Cx(x) = k(Ý,))
(3)
0.06
Our expectation is that generators with good rep- resentation of aleatoric uncertainty reproduce hu- man production variability along both axes. As we employ distance metric, it may look like we should regard model as good approximation to the human process whenever C+rrr) concentrates about small positive values. To some extent this is the interpretation exploited by most automatic evaluation metries (single- or multi-refeernce). In this work, we refrain from taking any one human production as a 'reference" to be closely 'matched'; rather, we take statistical properties of human pro- ductions illustrative of plausible variability and thus targets to be reproduced. We quantify devia- tion from plausible human variability by estimating notion of statistical divergence.
0.06
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0.0
0.1
0.2
0.3
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0.3
Semrnntcc Wassersten Distance
Figure 4: Distribution over Wasserstein distances for GPT-2 in blue: Dw, (C,(x), Hx)).. Distribution for a human control group in orange: Dw, (Hr()), Semantic probe: & is cosine distance.
difference is low for given æ. Positive differences imply that models overestimate variability, i.e.. model samples vary more with respect to one other than human samples do. Negative differences indicate that models underestimate variability.
Figure 3 shows how mean differences distribute across each task-specific test set for the models in Section 4. We use up to 10 human productions (5 for dialogue) and 10 generations. The first two show that (M,(w) - (aH+(w) distributes far be- low 0 for translation (OpusMT) and somewhat be- low 0 for simplification (Flan-T5), indicating that the two models substantially underestimate vari- ability. 8 The opposite true for dialogue and story generation: both GPT-2 and DialoGPT moderately overestimate the open-endedness of these tasks. We also inspect cross- coos-vvrrabilitty (C,(z) (x)) finding similar patterns, with slightly better over-
6.1 The Underlying Statistical Model
this section, we criticise the underlying statis- tical model (as result of parameter estimation via MLE) using unbiased sampling. As models observe variability only marginally (multiple refer- ences are rarely used during training), is interest- ing to study if their self- variabiiia is calibrated to human variability: given individual input instances, do distances between unbiased model samples dis- tribute similarly to distances between human pro- ductions? To distinguish over-estimation from under-estimation of variability, we report a signed notion of divergence, (Ma(w) - (z)( When M& (z) and (x) distribute similarly, their mean
'OpusMI uses label smoothing, which is known to harm the distribution of unbiased samples along dimensions such as n-gram and skip-bigram counts (Eikema and Aziz, 2020).

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Task Simplificatio
lask Translation
Unigram distance
Probe
bigram
Casine distance
Decoder
Unbiased
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Nacleus
Story Generation
Open-Domain Dialogue
Typical
Humen
Unigram distance
Terperature
Top k
Pobe
POS bigram distance
Costne distance
0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18
0.08 0.08 0.08 0.09 0.09 0.09 0.10 0.10
Mean Dwa(C(x),HM) H(X)
Mean (ML(C H[x])
Figure 5: Mean Wasserstein distances Dw, (C(x), H (xx) for (tasks, probe, decoder) tuples. Base models for each task are described in Section 4. Colour refers to decoding algorithm with various parameter settings (fully reported Table 4, Appendix F). Human control group in red. 1l Clusters suggest that decoders often have similar effect. Unbiased sampling is competitive.
all cross-variability calibration for translation and simplification (Figure 8, Appendix D).
we report a human control group (the orange curve); this is Dw, measured between two human popula- tions (i.e., we make two disjoint samples from the available human productions for each prompt, use those to estimate Hx) and an analogous Hz)()), and compute Dw, (H&(x), Hx(x))). We can now appreciate what is plausible Wasserstein distance curve between two human-based processes, and with that, we can better discern that this particular system gives good but not perfect representation to human levels of production variability (note the overlap between the two distributions). Upon vi- sual inspection of divergence distributions (like Figure 4) for different sampling strategies, we find similar shapes. We exploit this finding and sum- marise each divergence distribution using its mean. This is shown in Figure 5, which presents results for many decoding settings, tasks and probes. The left- most red dots indicate the human control group." We observe that two human groups agree more on the meaning of translations and simplifications than on their form, while for story generation the two groups agree more on surface form and basic struc- tures and less on the semantic content of the stories.
6.2 The Effect of Decoding Algorithms
We now study text generators obtained by varying the sampling procedure." We analyse their rep- resentation of uncertainty by assessing the diver- gence between the distribution of generator-humal cross-variability ((x) and human variability H (2:). While (C2(=) (H)( can inform us about the di- rection of miscalibration, we observe only a hand- ful of cases where different decoding strategies yield both under- and over estimation for the same model (see Figures 10 and 11 in Appendix D). In- stead, as we sometimes observe distributions with their difference in means paint incomplete picture-vy we additionally report measure of divergence that is more ro- bust such multi-modal distributions: the Wasser- stein 1-Distance Dw; (-; (x)). Results for self- variability M (a:) and mean distance can be found in Appendix D, Figures 9 to 11.
multiple
Human control group. The blue curve in Fig- ure 4 shows how Dw, (Cx(#, H&(x)) distributes over inputs for unbiased samples from GPT-2 on story generation. To contextualise this observation
Results. Overall, Figure 5 shows that most decoding settings are close to unbiased sampling, which in turn is in the same ballpark (mean Wasserstein distance always lower than 0.1) the human control. This indicates that text generators
"This leads a probability distribution whose pmf is hard if all possible to characterise, meaning we cannot easily assess the probability an outcome under the new distribution. But we have an explicit sampler for this new distribution, which is all our analysis tools require.
10 Inddee,, we find several cases where Dw. signals stronger miscalibration compared to D),. For an additional discussion about D see Appendix B.
II Noo control condition is shown for open-domain dialogue as the five references contained in DailyDialog ++ are ton few to create a control group.

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capture the space of plausible human productions well when coupled with most decoding algorithms, though not as well as another human language process. Decoding settings form many clusters, and for all tasks except open-domain dialogue, unbiased samples best match human variability. This suggests that, within the limits of decoding configurations typically considered as appropriate, different token-level decoding strategies often have a similar effect on generator's ability reproduce human production variability along our three probes. Altogether, these findings inform us about an often neglected aspect of decoding algorithms, namely their effect on the model's representation of uncertainty (rather than their ability to select individual high-quality generations).
reagiert', 'reagierten mit Zurtickhaltung" 'mit Vor- sicht reagiert', 'reagierten verhalten') while all ten generated samples contain the German phrase "vorsichtig reagier'' signalling that the generator's s lexical rephrasing abilities do not generalise to this imput instance. For text simplification, we focus on instances where Flan-T55's is uncertainty is not cali- brated to human syntactic variabilitty !3 We observe that simplifications sampled from the generator are always syntactically more similar each other than humans', indicating that the generator struggles to capture an important aspect of text simplification: that many semantically equivalent rewritings are possible if a text's syntactic structure is altered.
Variability overestimation in dialogue. Ac- cording to our estimates of human variability ($ 5), dialogue is the most open-ended task on all linguistic levels. We have hypothesised that this is due to the large variety of communicative act types plausible given any dialogue context. We have also seen that DialoGPT generally overestimates production variability ($ 6.1) 1 is one such example. Now we further inspect instances where cross-variability is miscalibrated with respect to human outputs." 14 We find that the generator's bad fit can be due to very short and generic responses (e.g.. 'Well..', 'haha', 'Ahem', 'Well done!"), but is more often due to the presence of fluent yet very diverse and often inadequate samples. For such instances, not only is the generator's cross- variability
7 Qualitative Instance-Level Analysis
We now qualitatively analyse individual inputs for which a generator's uncertainty is miscalibrated to human variability- as detected by Dw,- For each task, we use up to 10 human productions (5 for dialogue) and 10 generations. Figures accompanying the examples in this section are in Appendix E. While it is not replacement for more standard NL. evaluation procedures, argue that this level of analysis is complementary and crucial to gain deeper understanding of a generator's representation of uncertainty.
Variability underestimation in translation and simplification. We have seen that in translation and simplification, generators' velf-variability is lower than human variability ($ 6. I). We now zoom in on examples from these two tasks, inspecting instances that show inadequate model fit on all lin- guistic levels (i.e., Dw, (x)( Hx))))))))))))))))))))))))) (x)) is high for all k). The most severe cases of miscalibration for OpusMT are all instances of variability underesti- mation." For most of these, generations are virtu- ally or completely identical, while a few present slightly higher but still substantially lower vari- ability than human productions. For example, ten humans translated the phrase 'reacted cautiously" in the English source sentence 'Several companies have thus far reacted cautiously when it comes to hiring' in six different ways ('vorsichtig reagiert', "zurückhaltend reagient', "mit Vorsichtsmaanahmen
valf-aaaabbii too, is overestimated on all linguistic levels. In particular, the generator's poor calibration to lexical and syntactic variability is related its inability choose the correct dialogue acts (or favouring an excessive variety of dialogue acts). In an example instance where the last dialogue turn goes 'I've got a business call that really need to take', humans all reply with short affirmative responses (*Okay! Please.', 'Well! Go on.', 'Sure, why not!", 'Sure! Go ahead.', 'Yes! Sure.') while the model's responses are mostly lengthy statements, sometimes not particularly coherent ones (e.g., 'You don't 't need a business call. You need a friend").
Variability in lack of situational grounding. We have observed that human-wrinn wrrtterritnn stories in the BeitingPromplt dataset show lower variability than human dialogue responses, and hypothesised
We select instances with Dw, > 0.3 for unigram distance and Dw, > 0.2 for POS bigram and semantic distance. These thresholds are chosen based on distribution plots of instance- level distances (see, e.g., Figure 2b).
DD (M), (x)) 0.2: is POS bigram distance. DDw, (C), H2 (x)) > 0.2 for all & in (unigram dis- tance, POS bigram distance, cosine distance).

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Story generation, WritingPrampts "Wiite an entry for history book about an event in a fictional world all top level comments this prompt take the same world, make them all fit together."
0.8
GPI2-arge
(r=02)
0.5
0.6
0.4
Prdbabiity
0.5
0.4
0.3-
0.3
0.2
0.2
0.1
0.1
0.0
1.0
0.0
0.8
1.0
0.0
0.8
1.0
Lexical varisbillty (Unicram)
Syntactic variability (POS bigram)
Semantic variability (casine)
Figure 6: Example of poor cross-variability calibration GPT-2 with typical sampling on story generation.
that this may be in part due to contextual pressures that constrain variability ($ 5). We now analyse instances flagged by probe calibrated semantic cross variabiiryyyl for GPT-2. 15 For one of these, the prompt refers portion of the situational context the model does not have access to ('all top level comments in this prompt take place in the same world, so make them all fir together'). Because they are conditioned on and reuse that context, human stories are quite similar to each other; generations, instead, show much higher pairwise distance both when sampled jointly with the human productions (see Figure 6) and with themselves. The lack of relevant situational grounding makes the model more uncertain than it should be for this instance.
in humans. We find that NLG models overesti- mate production variability in open-ended tasks and underestimate it in more constrained tasks, and that most popular decoding algorithms all have similar, limited effect on the generators' ability reproduce human variability.
of badly
We advocate for more widespread usage of instance-level probing of NLG systems way to evaluate their statistical fit, not just along the dimensions we cover in this study but with respect to any other quality of interest. This approach contrasts with corpus-level analyses of NLG systems (e.g., Pillutla et al., 2021; Meister and Cotterell, 2021; Pimentel et al., 2022) and thanks to its greater interpretability. it builds trust in the ability of generators to reproduce human-like statistics when situated in specific linguistic contexts rather than 'globally', over a possibly heterogeneous corpus. In the future, we plan to devise new ways of improving the calibration of models' uncertainty (Zhao et al., 2022: Zhang al., 2022), e.g., steering generators with sequence-level decoding algorithms (Eikema and Aziz, 2022), and investigate the relation between uncertainty and perceived generation quality (e.g., Kuhn et al., 2022): while we use human levels of variability as a target, desirable levels variability may deviate from human statistics for specific applications.
8 Conclusion
Variability is an intrinsic property of human lan- guage production. Text generators, if they are to be considered good statistical models of human written production, should exhibit plausible levels of variability. However, in NLG, the widespread practice is (i) collecting only one 'reference' pro- duction for each input and (ii) evaluating only a single generation. To appreciate the impact of this incongruity empirically, we analyse multiple- reference datasets for four NLG tasks, and show that each task has its own plausible levels of lex- ical, syntactic, and semantic variability. We con- nect production variability to aleatoric uncertainty, the irreducible uncertainty of the language produc- tion process, and evaluate neural text generators in terms whether their representation of uncertainty is calibrated to the levels of variability observed
Future work should also study production vari- ability function of more complex notion of discourse context (Giulianelli and Fernández, 2021; Giulianelli et al., 2023) and attempt to disentangle communicative goals and realisa- tions (Stasaski and Hearst, 2023). This is an impor- tant avenue not only toward more practically useful generators but also toward reliable computational models of language production.
uncertainty
Dr,, (x). (x(x)) > 0.3; is cosine distance.

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Limitations
content, toxicity, or readability) that are considered relevant for any given application.
Our analysis relies on multiple-reference datasets, which are scarce for NLG tasks. Even though, for single-reference datasets, we cannot perform similar instance-level analysis, this fact does not entail that the observations we make do not apply we might simply not have the data to expose them.
Acknowledgements
We thank Arabella Sinclair, Claire Gardent, Clara Meister, Christopher Lucas, Ehud Reiter, Sarenne Wallbridge, and the ILLC's Dialogue Modelling Group for inspiring discussions. We also thank the MaiNLP group for feedback on earlier drafts of this paper. MG and RF are supported by the European Research Council (ERC) under the Eu- ropean Union's ' Horizon 2020 research and innova- tion programme (grant agreement No. 819455). JB is supported by the ELLIS Amsterdam Unit. WA is supported by the EU's Horizon Europe research and innovation programme (grant agreement No. 101070631, UTTER). BP is supported by the Eu- ropean Research Council (ERC) (grant agreement No. 101043235).
such datasets we
Impact of data collection. The way in which multiple references are gathered may impact the variability in productions. For example, asking a single annotator to produce several distinct ref- erences might artificially increase the diversity of responses. Conversely, asking several independent annotators might decrease diversity for they may resort to similar responses that quckly come to mind (or, in fact, the opposite if they interpret the linguistic context differently). To summarise, there are two levels of uncertainty in human production data: one is on the individual level, the other is on the population level. In this work, we do not distin- guish these two, although the analysis tools that we propose allow for it. For example, one could col- lect human productions from one individual (e.g.. for personalisation) from sub- hotb-populaiinss (e.g- improve fit for underrepresented communities).
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A A Note on Entropy
Entropy is an citrmattionthhoreete concept that is often used to summarise uncertainty about ran- dom variable. As useful as it may be in various contexts, entropy is not itself complete charac- terisation of uncertainty (e.g.. two different distri- butions may have the same entropy, yet represent different uncertainty about their respective random variables). As we discuss in $ 3.1, uncertainty about random variable is fully represented by its underlying probability distribution (Halpern, 2017, Chapter 2).
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Consider discrete random variable X with dis- tribution px and probability mass function (pmf) Px (z). Define the surprisal of an outcome X x as the quantity - loggxx()). Then, Shannon en- tropy (or just entropy for short) is defined as the surprisal of X taken in expectation under px (MacKay, 2003). Due to the unbounded sample space and lack of conditional independence as- sumptions, entropy is intractable to compute for neural text generators. In some cases a Monte Carlo (MC) estimate of entropy can be formed with a reasonable amount of computation. For ex- ample, consider an autoregressive language model that assigns probability (x;; 0) to a complete se- quence x using neural network with parameters O (e.g., an LSTM or Transformer). When we use ancestral sampling (Bishop, 2006) to decode from this model obtaining sample zf(e) the surprisal of (+) is directly available via - log fa; 0). and the sample mean - -/S ES log (a((s) 0) for an- cestral samples forms an unbiased MC estimate for the entropy of X. In most cases, however, the
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generator's pmf is unknown and the surprisal of an outcome is not available. That is the case, for exam- ple, whenever we employ decoding algorithms that bias away from the underlying distribution of the autoregressive LM-top-p,, top-k: typical sampling are all good examples. The resulting pmf is then hard (or impossible) to characterise.
mean divergences across tasks, probes, and decod- ing algorithms (complementing Figure in the main paper).
E Examples Discussed in the Qualitative Instance-Level Analysis
Figures 12-16 show examples of model fitness for the instances discussed in $ 7.
Furthermore, much as Shannon entropy can be interpreted in its informatioontheorette terms, it is not immediately obvious how can inform an analyst interested in the generator's faithfulness to human production variability. That said, the analyst may be interested in knowing, for example, that the entropy of the generator is similar to that of the 'human distribution' regardless of their ability to assign any useful interpretation to entropy proper. While accept some analyst out there may be curious about that question, we refrain from per- forming such an analysis ourselves because (a) MC estimation is not available for most of the popular decoders we wanted to analyse, and (b) estimating the entropy of the human distribution requires a faithful model of it (that is, we need perfectly faithful text generator to play the role of 'gold standard').
Decoding Configurations
Tables 2 to 5 show mean divergences for all the analysed decoding strategies, in terms of Wasser- stein 1-Distance well mean distance.
B A Note the Wasserstein 1-Distance
The Wasserstein I-Distance Wi(:.) quantifies a notion distance between two probability measures and is particularly convenient for it can be estimated using Dirac deltas (samples from those measures; Peyré et al., 2019) more easily than alternatives such Kolmogorov-Smine and total variation distance (which require binning the measurements into empirical cdfs/pdfs). Wi (I), and (CxC) Hz(x)) have an interpretation in terms of "mass" (in units k) that has to be moved, on average, to transform one set of samples into another.
C Data Statistics
Table shows relevant statistics for the four multiple-reference datasets presented $ 4.
Additional Figures
Figure shows human production variability over lexical and syntactic unigrams, bigrams, and tri- grams (complementing Figure 2 in the main paper). Figure 8 shows the distribution of (Cz(z) (H)()) over instances for our four tasks (complementing Figure the main paper). Figures 9 to 11 show

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Mackine Transdation
Test Simplification
Story Generation
Open-Domoin Dhologue
MEAN STD MED.
RANGE
MEAN 1 STD
MED.
RANGE
MEAN 2 STD
MED.
RANGE
MEAN 1 STD
MED.
RANGE
23.34
8.92
25.40
17.62
Iaput
Tokens
25.79 12.91
4.81
28,00
11.68
26
7:78
26.49 14.68
24
1-70
48.94 31.52
5-326
Sentences
1.01
1-2
1.75
2
1-6
2.82
1-22
Words in sent.
23.15 11.37
2-67
21.80 9.11
1:57
14,48.1
14
1-50
8.67
5.20
Tokens sent.
25.58 = 12.90
23
2-81
27,42 11.88
25
1-78
15.12 8.13
14
1-51
8,33 +.39
1-50
8.29
Outgot
Tokens
27.28 14:09
S-86
24.22 10.65
22
5:91
696.66
476.93
$70
104-2961
10.84 + 5.01
10
2-53
38
Words in sent.
20.67 = 10.86
19
1-66
14,70 6.71
1.39
13.81 + 9.59
12
1-722
8.06 + 4.32
1-36
Tokens
25.69 13.92
1-86
18.19 à 6.78
1-91
14.63 10.22
12
1-722
8.24 4.45
2:37
Table 1: Length statistics. Number of tokens obtained with the tokenisers of the language models used for generation.
20
lak Smpplication
Smoliiicaton
2.5
Tersation
Genershon
Consratios
30-
Open-Domain
2.5
Probabiloo
Probebblityy
33
0.4
0.
1.0
0.2
Lexical (bigram) variability
(b) Lexical (trigram) variability
tncc
Simpificacion
Simpliicatinn
3/1
Tansiadon
Trassiation
Story Gineratioo
03
Stry Seneration
Open Comsin Dulogue
Spen Domats Disiaque:
2.3
0.2
0,2
3.0
80.
(c) Syntactic (POS unigram) variability
(d) Syntactic (POS trigram) variability
Figure 7: Human production variability across four NL.G tasks (the remaining settings not reported in the main paper). The values the æ--xxi are single samples of lexical or syntactic distance between two productions for each input (see Section 3). Probability mass on the right side signals high distance and thus high variability, and vice versa. A large spread indicates that production variability varies widely across inputs, and such that task does not define a specific level of variability.

---

Lexical variabllty
Syntactic variabillyy
Semertic variablitt
Trandaaioon
-0.0
-06
-06
-02
0.
02
04
0.6
0.6
-0.
-0.6
-04
-02
00
02
04
0.6
0.8
-0.6
-0.6
-04
-02
00
0.2
0.6
0.6
0.6
Stor
-0.
09
Onlogse
-0.0
-0.0
+0.6
-0.2
0.0
0.2
006
0.6
-0.
-0.6
+0.4
-02
0.0
04
0.5
0.
-05
-0.6
-0.4
-0.2
U.0
0.6
06
05
Urigram oblance
POS slgram distande
Costno distanc
Figure 8: Distribution of PC2(2) (x)( over instances. Values greater than zeto indicate the model overestimates the variability of the task (higher mean pairwise distance); values below indicate variability underestimation.
Simplification
Task Translation
Unigram
Probe
POS bigram distance
distance
Decoder
0.05
0.10
0.15
0.20
0.25
0.05
0.15
0.20
Nacleus
Task Story Generation
Task Open-Domain Dialogue
Typical
Human
Unigram distance
Temperature
Top-k
POS bigram distance-
distance
0.03
0.05
0.08
0.10
0.12 0.15 0.18
0.10
0.10
0.10
0.11
0.11
Mean (MX()) HIX))
Mean Day (MX))
Figure Mean Wasserstein distances Dw, (M(z), (x)) for (tasks, probe, decoding algorithm) tuples. Base models for each task are described in Section 4. Tuples that share colour have different decoding parameters. Human control group red; except for dialogue, where references are too to create a control group.
Task Simplification
Task Tra Isanstatoon
Unigram distance
POS olgram distance
Cosine distance
Decoder
Unbiased
0.25
0.20
0.15
0.10
0.05
0.00
0.25
0.20
0.15
0.10
0.05
0.00
Nucleus
Task Story Generation
Task Open-Domain Dialogue
Typical
Human
Unigram distance
Temperature
distence
Cosina distance-
-0.05
0.00
0.05
0.10
0.01
0.07
0.03
0.04
0.05
0.06
μM(x ph((x)
jM(x) (H(X
Figure 10: Mean of distances (M()) (*(*)) for (tasks, probe, decoding algorithm) tuples across test sets, Base models for each task are described Section 4. Tuples that share colour have different decoding parameters. Human control group in red; except for dialogue, where 5 references are too few to create control group.

---

Task Simplfiicttin
Task Translation
Unigram distance
Probe
POS bigram distance
Cosino distance
Decoder
Unblased
-005
-0.04
-0.03
-0.02
-0.01
0.00
0.02
0.04
0.06
0.08
Nucleus
Task
Story Generation
Task Open-Doman Dialogue
Typical
Human
Unigram distance
o.o.
Temperature
Top-k
POS bigram distance
Cosine distance
0.05 0.08 0.10 0.13 0.15 0.03
0.05
0.07 0.07 0.08 0.08
Mean (C(x) - (HH())
Mean UClxI - (H(())
Figure II: Mean of distances (C(x)) (M(()) for (lasks, probe, decoding algorithm) tuples across test sets. Base models for each task are described Section 4. Tuples that share colour have different decoding parameters. Human control group in red; except for dialogue, where references are too few create control group.
Machine translation, Newstest2014:
"The Town Councils have decided that the answer is yes."
1.0
Humans
1.0
Opus MT (a=1.25)
0.8-
0.8
Probability
0.6
0.5
0.6
0.4
0.4
0.3
0.2
0.2
0.1
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.0
0.2
0.4
0.8
0.8
1.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
Lexical variability (unigram)
Syntactic variability bigram)
Semantic variability (cosine)
Figure 12: Example of bad fitness (Dw; (M, Hz(z))) human variability for the Opus MT model.
Machine translation, Newstest2014
"Several companies have thus far reacted cautiously when it comes to hiring."
0.8
Humans
0.8
Opus MT (a=1.25)
0.8
0.6
0.6
0.6
Probability
0.5
0.4
0.4
0.3
0.3
0.2
0.1
0.1
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.5
0.8
1.0
0.0
0.2
0.4
0.6
1.0
Lexical variability (unigram)
Syntactic variability (POS bigram)
Semantic variability (cosine)
Figure 13; Example 2 of bad fitness (Dw, (x), Hx(x)) to human variability for the Opus MT model.

---

Machine translation, Newstest2014
"Until just recently, really feel sorry for how much grief caused other people,"
0.7
MT (o (1.2.
0.6
0.5
0.4
0.5
Probabiltty
0.3
0.3
0.2
0.2
0.2
0.1
0.1
0.0
0.0
Lexical variability (unigram)
Syntactic variability (POS bigram)
Semantic variability (cosine)
Figure 14: Example good fitness (Ow, (M&( Hz(x))) to human variability for Opus model.
Dialogue, DallyDialog++
"Would excuse me?". "For what?" "l've got business call that really need to take."
1.0
Humans
DialoGPT-medlum
0.8
(p-0.9)
0.8
0.8
0.6
Probability
0.6
0.4-
0.4-
0.2
0.2
0.0
0.0
0.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0,2
0.4
0.5
0.8
1.0
0.0
0.2
0.4
0.6
0.8
variebility (unigram)
Syntactic variability bigram)
Semantic varisbility (cosine)
Figure 15: Example of bad fitness (Dw, (Cx()), for DialoGPT-medium.
Dialogue, dailydialog++
"Am I too abrupt? Did scare you? Ör you don't want..." "No! have been expecting this moment long time. mean my answer is 'yes'." "Oh, I'm happy. will try my best to love you and protect you. promise."
0.5
Humans
0.4
DialoGPT-medium
0.4
0.4
(p-0.9)
0.3
0.3
Probabiitty
0.3
0.2
0.2
0.1
0.1
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
D.4
0.5
0.8
1.0
0.0
0.2
0.1
0.6
0.8
1.0
Lexical variability (unigram)
Syntectic variability (POS bigram)
Semantic variability (casine)
Figure 16: Example of good fitness (Dw, (C&(z), ( HA (x))) for DialoGPPTmeddiu.

---

Table 2: Mean D H())) results for different decoder settings.
Prabe
Pays (MM))) (irl)
dunt5 Juse. Sneusel ancestul
Usignan disunce
0L075174
Staplifiazio
0.114967
Simpliicaans
large fnerrss-mmgles
Inigam
Simpinoeen
donTS luge. Sneuned opkaa 02
Uaiguon disunce
02171277
Stupliictton
JentS_Jerss__a htwp_fastnnd-opiallooee 005.al
Uaignan diaann
0,091199
Siuplifizaan
human, commi
Liigne distnce
014161
Simpihormn
nignan distane
0000166
Simpificemn
dtnnts lare finemmeed miters (l-
POS higrom dissance
11122299
Simpiitation
duit5 hatas//ees..asue--mnaaaa.a
Sanuuse mndeas (65 val
POS bignan disiacce
01108829
Sisplilaziu
(asii Jangs www.ssnnncssppci..oooooo Oloval
POS hignan distance
01244077
Simpliheein
itnnts large anemmed eypical 095
POS higrom desenne
11 1100179
Simpitcarin
humonn commml
POS higrom disennee
(109222)
Simpitceunn
dann5 larg. Snnuseed ancestal vil
Cosite disunce
01011924
Simpiiiation
daaiSS fesss_ssss__stsdsraaaaa suctund--ncccaaa 59.xal
Cooise disaaoe
01055711
Sispiiiizia
Mantiy, Jary. ww_.Snnnet--nnkkn (955-l
Crore diewnot
0001541
Simpiheeuu
dantt large fineueed opical 02
Cosite
0
Simpiiiction
JastS_Jese wecteeeetuusseeeeooooo wtwp__atttcl-ppppal0ff0 -0plal0000ll
0,051225
Coite
0.02563
ugus-ansziral
01250346
Inigam decines
opes mucless C9 upu cro-Eemppnattd:00
Usigran disance
0.362719
Tunlakk
Uaignan disans
0.212019
Traelaion
opu 1c5pprutte05555
Uaignnn dalans
0.221174
Tuelaika
spu--iquk_330 opes top k 40 cottan
Laigam distenz
0.251994
Tomistio
Unigam disunce
0131100
Tondation
Uaignan dilance
0,043103
Tueliioo
PCS hignan distance
01155665
Inaiiiion
opes miceos 088
POS higrom desinnee
01 017366
Tomiiiion
ope- pre-mmciooc.o 09
POS higrm diesncee
11 1655579
Tomimoo
tecppeuuees
POS bigrun disince
0155774
Tuminioo
PCS bianan diiaace
01181173
Tuslaiio
opte-trp._.00
P08 bignin desence
Toniiion
4.40 conmol
0155915
Teniiion
dissnnce
000500
Tonimen
Cuise
0
Tuslaks
111474
Tnadaino
open
opes tecpeuuee25
Cuite Ussaae
0.441220
Tunlako
Cuoine Gitaaso
01141175
upu 1oy_3_
Cuise Sstanee
0133558
Tualaiks
Criree dietnao
013181
Tondstion
human conmol
Coslte disunce
0107766
Tondation
s012 [e__Sage__issttn Ristuiisteeeleeeeee vos
Uaignan dasanne
0
Stay Genmuaio
upt bege Instanskenniin Wirz
Unigzam distenz
01081405
Story Grraraiio
apil bays ht____e___niiissaaaa Jinziank-uuciss M15-usl
Laignan diatanzz
[1082663
Story Guaasinn
ge3 bege marreeddmmmpninnttteee
Lnigoam dietnne
11 1156600
Strry Grreraion
2012 Srgge.gee.... Ristuned typccll 02 lest Ioatan
Uaigium disunce
0,085595
Stor Generatio
Usignan disiane
11025115
Story Ganaiioo
PGS bignan desince
1
Strry Gorenation
fistuned
01112113
Generation
fisttned 055
0111260
Generation
fisttned jeapenuueeff
0.192448
Genetation
agtl_ www.innnnnncoiillllaaaaaa
01007114
hignin
Stry Genecation
get Jurge fistuned ancestal test
Cuite üisaee
0.095689
Story Genetatio
2e1 lagg Rittuned
test
Bssace
0100160
Story Genetation
apl2 Laree Incionsl-succaooooee 035-uai
Cuise Sstanee
0.093502
Stay Gruaaaio
geut, bege, Inntorelssmmeemmistter
Cnire diesnor
01421144
Story Grnaaaion
get aage fietneed typical 02 text
Codite disunce
0110066
Stoy Generation
banan Sontenl
Cuise ästave
0.053663
Stayy Genaraiu
dusagpe Longeinnoerreeeee
Unignam distene
0.994765
Opan-eemain Daskgge
haman sooetnl
Laienan diatanz
SWN
OpesDenain Daiigu
dasigge longe-tnceen molder
Enigzam dietnnze
Derrain Dhvingge
dazloge Jurge aucles 095 dev
Uaigium disunce
01091945
Open Darzain Diálgge
Jange-op. 30-aey
Laignan disaane
[L091910
Open Denain Diaagu
desiepe
Unigam dieinee
0101104
Demain Davingon
Laugam distns
1110022
Opa-lenain Dasigar
daiogge lorge typical
Unigam
01919912
Open Domain
Jane
Open Denain
Opa--anain
ducogge lorge
hignin desensee
Open
Davlgge
dillope Jurg auciou
POS bigran dsuace
0.105007
Open Denzin Dillgge
dicogge lurge xop dev
higran disunee
01107674
Open Donzaln Dallgge
POS bianan dalanne
0.107663
Open Denain Dallgus
daskoge longe-tyicall (itdde
POS hyynan dietens
0.11599
Ope--Demain Davingee
dislgge large typical 185 der
POS higram distacce
105555
Open Dorain Dillogee
daduyy Juse ascootalddes
Cuise disave
0.112806
Open Denain Dalius
bunan contool
Cuise dsuace
N,8
Open Donain Didlgge
dadupt Jange-nddoo, Middey daingge lnnge-tnceen not-dar
Coine diease
TL112680
Openn Daligue
Cndree dresnce
11111765
Dorain Davingge
dadagpe Jurg sop R. 30 dey JankepJanggknne Jangs-op. Jo. daingge Jonge-tpiial m0-ee daingee Jong-ppang- dey
Cuite diunee
01111221
Open Danzan Didlgge
Cloine disaae
[1113160
Open Danain Diaagu
Cuure diesvor
B1119
Domain Daingee
Coire dienas
11100445
Ope--Demain Daauger

---

Table 3: Mean (x( H())) for different decoder settings
Prabe
(M(M)[s) M[r))
dunts Juse. Sneusel ancestul
Usignan disunce
0.041574
Siepliiiation
Simpliicaion
barge fnerrss-mmlees
Inigam
Sirpiiocn
donTS luge. Sneused opkaa 02
Uaiguan disunce
0314236
Siepiikaaio
BaiiT_Jeess Jans, SaniS_Jee___sssnnoopppp www._nnsnndoopoolo0555553 005.al
Uaignan diaann
Simpliiezio
human, commi
Liigne distnce
Simplhceem
nignan distnne
01045417
Simpliizeiin
dtnnts lare finemmeed miters (M-
POS higrom dissance
0.110000
Simplitcaton
duit5 Juse. Sanusse madeas (65 val
POS bignan disiacce
0.089422
Sieplikation
(asii Jangs www.ssnnccssppci..ooooo Oloval
POS hignan distance
-1.247776
Simpliccion
itnnts large anemmed eypical 095
POS higrom desenne
-100770
Simpitionin
humonn conmml
POS higrom disenne
Simpitoem
dann5 larg. Snnuseed ancestal vil
Cosite disunce
0.016417
Sinpiitction
daaiS Sesoss_ees__studsrmaaaa Sudtund--ncccaaa (9..al
Cooise distas
.0049492
Simpliszzio
Mantiy, Jary. ww_.Snnnet--nnkkn (955-l
Crore diewnot
-0090225
Simpliicein
danit large fineueed opical 02
Cosite
0.042222
Simplitiation
Jast5_Jese wwwte_ectussseeeeeeoe huwp.facteniloppial..ff -0plal_000lll
0.043586
Siupliiczioo
Coite
0.005045
Siuplilizzion
ugus-nnsziral
-0240243
Trandnicc
Inigam decines
opes muclesx
Uaigran disanse
Trandation
upu cro-Eemppaaatd:00
Uaignan disans
Trandticss
opu 1c5pprutte05555
Uaigann dalans
0231999
Trunktics
spu--iquk_330 opes top k 40 cottan
Laigam distenz
20255385
Unigam disunce
0250043
Trndine
Uaignan dalanc
0.02318L
Trandaiit
apo--nnzzizal
PCS hignan distanee
4.151919
opes miceos 088
POS higrom desince
Tondaner
ope- pre-mmcionc.o 09
POS higrm diesnse
11161130
Tondmor
tecppeuuees
POS bigrun disunce
0.152725
Tenduict
PCS bianan diiance
a.175065
Traniiict
opte-trp__.00
P08 bignin desence
4.40
0.15322
Tenduton
conmol
dissncee
0,05000
Tendmer
Cuise
0.129007
Trasaiia
-0133000
Tradhaicn
opes tecpeuuee25
Cuite Ussaee
0.34733
Truslatin
Cuoine Gitaas
..147533
Tranhtics
upu 1_p_33
Cuise Sstanee
..132567
Truaktics
Criree dietnao
..19499
Traditic
huran conmoo
Coslte disunce
0.88674
Trndiiee
s012 [e__Saye__isstte Rasgjiistteeeleeeee oos
Uaignan dalane
0.00227
Stary Guoention
upt bege Instannkeniinn Wirz
Unigzam distenz
-U021124
Story Generaioo
apil bays htt__an___niiisssaaaeea Jinzians-uuciss M15-usl
Laignan diatanzz
-0011460
Story Ganandion
ged.. bege marreeddmmmnnnnnttteer
Lnigoam dietnne
-1341100
Story Gireration
2012 Jarge.ee Ristuned typccl 02 test Ioatan
Uaigium disunce
0.42259
Story Geteratio
Laignan disaans
.0005585
Story Graaniio
PGS bignin detinee
0.9940
Greezatin
fistuned
0.05952
Geteration
fistaned 055
0.66751
Gerecation
fisttaed jeapenuueef
0.13673
Genenaiin
agth_ www.innnnnncoiill.laaaaaa
0103302
hignin
Stry
get Jurge fistuned ancestal test
üisaee
0.007763
Stary Geoention
2e3 lagg Rittuned
test
Bsuace
0015119
Stary Generation
apl2 Lagee Incionsl-succaonooeee 035-uai
Cuise Sstanee
.0004399
Stary Grssdiio
geut, bege. Inntrredssmmpemmastterr
Cnire dieanoo
-0065303
Story Grnezati
get aage fittnned typical 02 text
Codite disunce
0338562
Story Geteration
Sontrol
Cuise ästave
0.003839
Stary Gueeutiin
dusagpe Longelnnoerrdleee
Unignam distene
0.099736
Opessoniii Doingie
haman sooetnl
Laienan diatanz
N&N
Opo-Saaiis Delogee
dasigge longe-tnceen molder
Enigzam dietnnze
Ope-toenii Dalegse
dazloge Jurge uucles 095 dev
Uaigium disunce
0.051555
Open Donair Didogge
Jange-op. d0-
Laignan disaans
0.944522
Opn-Donsi Didugae
desiepe
Unigam dieines
Qpe-tommair Deingse
Laugam disins
Opn-tonaii Deiugse
daiogge lorge typical
Unigaam
01512477
Domair Dasogge
Jane
0.040550
Open Danais Didogue
Opa--onaii
ducogge lorge
hignin desinsee
Spo-Donall Dallggee
dillgpe Jurg auciou
bigran Esuase
0.034128
Open Donait Dilogue
dicogge lurge xop dev
higran disuce
03036113
Open Donait Dilogge
POS bianan dalanne
0.036636
OpenDaraai Dadugue
dasigge longe-tyicall (iDdeo
POS hiynan dietens
Opeslomair Deingee
dislgge large typical 195 der
POS higram distacce
0.035666
Open Donair Dislgge
daduyy Juse ascootalddes
Cuise disave
0.061600
Open Donsi Dadogae
bunan contool
Cuise dsuae
N:N
Open Donsi: Dilogee
dadupt Jange-nddoo Middey daingge lnnge-tnceen noe-dar
Coine dieass
0.045279
Opesi Deinge
Cndree dieonce
01155001
Opn-ienair Daingse
dadagpe Jurg sop 3.30 dey JankepJanggkop Jangs-op Ja. daingge Jonge-tpiial m0-ee
Cuite diuaee
0.003337
Open Donaie Didogue
Cloine disaae
0
Opu-Donii Didugae
Cuure diesvor
0106671
Ope-toomair Deingse
daingee wong-opccall dey
Coire dienas
(185418)
Opn-toomair Deiugse

---

Table 4: Mean Dwi(C(1)) (z)) results for multiple decoding settings.
Madel
Probe
Meon Dar (C(e), Hi
Tink
danTS lurge freunee oncestall
Unigumo disunce
Simpliiactton
Vaignan daaanz
Simpliiizzios
Banti Jerg. hrrp-frnmmms-mmmii (95-al
Laigam disonee
Simplteem
danits luge fineused opiicll 02 val
Unigam disunce
0.05966
Sirplikaaton
duit5 Jaass waw..uunn--ppchcll
-yppaadd-ppaa_000----0 (00 val
Uaignan disance
0355422
Sinplikzzion
buran control
Uaigian disianee
0.842963
Simplikzzonn
POS bignan disiance
01856443
Simpiieziin
Hannts large macler (Wkxsl
POS higrom dissnne
0000660
Simpliteooon
dant5 lage. Sneused micless 595 vil
POS bigrun disuncee
0.061433
Simplifiatoon
dasiSS Prus_fostund-pppi. 02-val
PCS bianan diviance
0.075297
Simplitzaion
daniS Jaape_Jerpee www..oond--oopoaoooo. Aypioal_ 095 val
PCS bianan disiance
0.061624
Simpliizaioo
human contni
POS bignan distanes
0109922
Simptiiztion
danits finemee ancestal
Conire
01077947
Simplitation
Juse Sueuuuueesssleo audkass000000
Sinpliizzion
dtnnts barge finersett repictt-pppca
Simplticatoon
barge mrenns--yppoil..o
Simpiticem
ccntrol
Cosite disunce
Sinpliicatoon
upua anssial
Vaignan dalano
0367614
Tuadation
Lnigam distene
0009120
Tandation
opes macees open troperomenoo
Unigam disunce
0003666
Tradatco
Unigam desince
011560
Tradation
opes tesperanes7755
Uaignan disunce
0
Tundaion
upu-diq k_30
Usignan diatans
0,068022
Tnadation
apee-app F 4:40
Lnigam dievnne
(1009977
Tondanen
humen
Usigiun disunne
0.043192
Tundation
PCS bignan diviane
0.855636
Tradation
085
PC6 bianan dillnce
03057639
Tuadation
upo-melons.99
P08 bignan distnnes
Iradation
0104674
Tondation
0.058195
Tundatoo
0.05540
Tnadation
hignin
opes ancestall
dsuace
Tradatio
upu 985
Cuosse Gstano
Tradatiio
Oriiee dievnce
0041211
Tranation
opes teopeeaaeee
Cosite disance
0112500
Tradatoo
opes tropecomennt
Codiee disunce
0B152
Trandtion
opes to. 3_33 upu-diq___40
Cuise Ssuaee
0.843564
Tundaion
Cloine dieaas
00022113
Inaddation
human ccniml
Cendre drevnce
Tondation
2012 lage, Ristaned ancestal test 2012 lrge. Rattuned toceos, 69 tes: apt2_ succias 075-ual gett, bege, thettned-smmeennnthherr
Uaigran disinnee
0,052815
Stoy Geoeraton
Unigram disiance
0,052935
Story Generatio
Uaignan divance
0.053015
Stny Geuavaio
Lauyam ditines
11811165
fiseuned opicall
Unigam
01080575
Gonecation
Generation
gett. bnge
higpin
20d Juge, Rittuned rempenuteett test
bigran disanee
0.180557
Geteration
POS bisaa dasianos
0.07543
Stay Generaio
human, commi
POS hyrmm dietrne
Stzy Grrezioo
gotl bare, enetonol-nneoeeeoooi
Cuise diease
0103816
Stayy Genertion
ged bage mnetoned rodeos 09 rest
Codiee dieunce
007773
Stzy Gereration
gor2 Jage, listtaned audeu, 095 usi
Cuise ásuve
0
Stay Geoeratin
upil baye, Inciinsscccmeennneee-444
Cooine dieane
0005220
Story Gunarati
ged bege morraned rypical (1 see
Codree drewnce
Stoy Geeeration
hamen ccctral
Cuite disanee
0.050663
Story Geoeration
dasogge lirge anceomal dev
Uaigiam disincee
0.092540
Open Donain Dalogte
Laignan disans
SGN
Open Denain Didogux
dasngpe wow.eeeeennnn Wildey
Laugam disinns
Open--onain Dicioga:
large mxcleus 095 dabagtt J. 3.30
Unigam
Denain
Diáoguue
daboggt Jarge
0.082093
Domais
dosngge tngg--ypicat
Unigamm dicince
Open Diinggs
dilogpt Jurg ancestal
bigran dounce
Open Dilogte
hanan
POS bianan disaano
Opan Danais Didogue
dalogge ionge-meten, Biider Jongommnnnnn IWS-der duslogee large top
POS higram diesrnce
Ope-Dorrai Disigge
hignan distanss
01841133
Opa-Dorrai Disogge
der dillgpe Juge jop_k_o dey dankeyyJonggiitn Jonge-tspccll..e.. Lib-lee davigge lorge rppicll ins dey duvigge Inne-nnceerriddr human, conmol
POS higram dissacce
Open Denain Disogge
POS biznan distacce
0.083804
Open Denais Didlgue
POS higsan distance
00051199
Opa--Denata Dedugs:
POS higro disinnee
01095661
Open Demain Daiogne
Crnree drennse
(1074410
Opm--Demain Davingn
Coite disance
N:N
Open Donain Dilogte
dadkupt Jang andeas mider desigge meneee nos-der
Cooise disaaee
0.094-12
Open Donain Didogux
Cuire dienna
Dpen-nenain Disiogae
large
der
Codite
0185622
Denain Diiogne
dabagye 02
dabogp
Coite
0.051005
Donais
Opas-

---

Table 5: Mean (z))) results for different
Hz))))
decoder parameter settings.
Prabe
dust5 Juge. fneusel ancettal
Usignan disunce
0,019150
Suppiiicaioo
40047466
Sugliiiatioo
large fnerrst-mggees
Inigam
Smplittcaion
donTS luge. Sneused opkaa 02
Uaigunn disunce
0047560
Siplittction
SaniSSJee___sssns-opppp www.:uisnndoopolll0555553 005.al
Uaignan diaann
00088222
Supliiittion
human, commi
Liigne distnce
40004517
Smplittztionn
nignan distnns
40024311
Saplitzaionn
dtnnts lare finemmeed miters (l-
POS higrom dissance
00001134
Siplittcatonn
duit5 Juse. Sanuuse mndeas (65 val
POS bignan disiacce
00041670
Sapliksuio
(asii Janps www.ssnnccssppci..ooooo Oloval
POS hignan distance
40090730
Suplitction
itants large anemmed eypical 095
POS higrom desennee
-0000991
Smplitcction
humonn conmml
POS higrom disense
200081
Suplitzztion
dann5 larg. Snnuseed ancestal vil
Cusite disunce
0:001191
Suplitcation
daaiS Sas.ssseee__stuesrmmaaa sudtund--ncccaaa 59-xal
Coois disaao
00021733
Suplisction
Mantiy, Jary. wu__Snnned--mmkkn (955-l
Crore diewno
<0019/644
Supliscation
danit large fineueed opical 02
Cosite
0/027118
Siplitfcaiion
Jast5_Jese wewteeectussseeeeeeooo wawp_factusl-lpppal.fff -0pl0l0000lll
00017714
Supliscaton
Coite
0.000045
Supliliction
-0081401
Transaai
Inigam decines
opes
Uaigran disanse
0033062
Tassiioo
upu cro-Eemppaattd:00
Uaignan diaans
0.009966
opu 1c5pprutte05555
Uaigan dalans
.0028734
Tansliion
spu--iquk_330 opes top k 40 cottan
Laigam distenz
40051315
lantem
Unigam disunce
0001331
Tarsitoon
Uaignan dalance
0.023181
Tasiiion
PCS hignan distane
20010:54
Tambeim
opes miceos 088
POS higrom desince
0001130
pre-micionc.o 09
POS higrm dissnse
200000
Tariem
tecppeuuees
POS bigrun disince
0:001551
Tarskion
PCS bianan daiancz
00004488
Tamain
opte-trp__.50
P08 bignin detence
<0010525
banbem
0010559
Tarskaoon
conmol
disincee
0.009300
Cuise
0.014922
Tassiio
-0015346
opes tecpeuuee25
Cuite Usuaee
0076953
Tassiio
Cuoise Gitanoe
0013291
upu 1_p_33
Cuise Sstane
0014714
spo-dip_1.44
Cniree dietnao
40014077
Tantem
huran conmoo
Coslte disunce
000964
Tarsiaion
s012 [e__Say__isssttn Rastjiistteeeeeeeee vos
Uaignan dalane
00346522
Sun Gesontion
upt bege Instannkeniinn Wirz
Unigzam distenz
0020188
Sun Gesenlion
apil bays ht___/e___niiisssaaaaea Jinzinnk-uuciss M15-usl
Laignan diatanz:
0031654
Sun Oeaenlion
ged.. bege marreeddmmmpninnttteer
Lnigoam dietnne
000966
Soy Lesritiinn
2012 Jarge.ee.... Ristuned typccl 02 lest Ioatan
Uaigium disunce
0023071
Sony Genention
Laignan disans
00200558
Sun Genendion
PGS bignin detinee
00000
Genmmioon
fistuned
0073412
Genention
fisttned 055
00090008
Genemtion
Sigte_ieettned jeapenuueeff
0.175454
Gesention
agth_ hww..nnnnnooiiil.l.......
0089997
hignin
Sony Gesration
get Jugg fistuned ancestal test
üisaee
0.001377
Suy Gesention
2e3 lagg Rittuned
test
Bsuace
0047100
Sony Gesenaiin
apl2 Jaree Incionsl-succaaoooeee 035-uai
Cuise Sstanee
0.04877
Suy Gesention
geut, bege. Inntrredssmmpemmistterr
Cnire dieanoo
00081529
Smny Genemtiin
get aage fietneed typical 02 text Sontarl
Codite disunce
0015111
Sony Gesention
Cuise ästave
00000539
Sun Gesznniin
dusagpe Lopeelnnoerrdleee
Unignam distene
006/000
Open-Samin Dolagi
human soottnl
Laienan diatanz
N&N
Opan-Damin Dallegac
dasigge longe-tnceen molder
Enigzam dietnnze
COMMIS1
Opee-Dmmm Daalggir
dazloge Jurge uucles 095 dev
Uaigium disunce
0.009299
Ojen Dunan Dialogue
Jange-op. d0-
Laignan disaans
0.264558
Opa--uunnn Dilaga
desiepe
Unigam dieines
Ope-lbemn Dalogur
Laugam disins
0017/55
Oper-bemnn Dallgu:
daiogge lorge typical
Unigaam
0.068922
Opre-Denot Dalogir
Jane
0.000752
Dillguu
Opan-2mmmin Dallgg::
ducogge lorge
hignin desence
Opre-Demin Dallggi
dillope Jurg auciou
POS bigran &suane
0.047550
Opes Domai Zialoguz
dicogge lurge xop dev
higran disnce
0.16732
Opee Donain Dialogue
POS bianan dalaaned
0.001399
Optr-Dunii Dislagec
daskoge longe-tyicall (itdde
POS hyynan dietenss
0083058
Ope--bmn Dalogic
dislgge large typical 195 der
POS higram distacce
0088111
Open Denah Daalogue
daduyy Juse ascootalddes
Cuise disave
0078752
Opae-Dunis Dillogac
bunan contool
Cuise dsuae
NaN
Opze Dunai Didlogue
dadupt Jange-nddoo, Middey daingge lnnge-tnceen not-dar
Coine dieass
0079942
Opan-Dmmmn Dallggac
Cndree dresnce
007515
Ope--Dmmm Dallgger
dadagpe Jurg sop 3.30 dey JankepJanggkop Jangs-op. Ja. daingge Jonge-tpiial m0-ee
Cuite diunee
0073973
Ojes Danin Dillggu
Cloine disaae
0076133
Open-Dmnnn Dilaguz
Cuure diesvor
00/3161
Ope--benn Dalogur
daingee Jong-rpcccll dey
Coire dienas
0014361
Ope--benn Dalegu:


Reviews:
[{"review_id": "MKe3sMou5X", "reviewer": "Reviewer_haSd", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "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": "Thank you for such a detailed review! We are happy that you consider our framework as a valuable tool for NLG evaluation, and we are confident we can address your comments in the camera-ready version of the paper.\n\nWe will incorporate your editing suggestions and improve our terminology. In particular, we will use the additional page to clarify the connection between uncertainty and variability. As we attempt to explain in Section 1 (lines 68-76), the term variability implies that there are multiple possible responses given an input. An NLG model's representation of uncertainty should ideally reflect this with a wide distribution over the strings that are plausible according to a population of speakers. Thus, the term “variability”  is closely related to aleatoric or data uncertainty, which is uncertainty inherent to the data that a model is trying to capture, rather than the model itself (this is often called epistemic uncertainty).\n\nWe used “fitness” to refer to a statistical model’s ability to fit or reproduce (characteristics of) the data. We will use “model fit” instead.\n\nWe are also happy to include a discussion of your comment _“Experimental results seem to benefit open-ended information as reported by the authors.”_ if you please clarify for us why you perceive this as a concern rather than as an empirical finding.\n\nReproducibility is important to us. In the camera-ready, we will include a link to a public repository with the code to reproduce all experiments in our paper. To further aid reproducibility, (1) we will upload our fine-tuned models to the Hugging Face model hub and (2) we are considering the additional release of the large dataset of model generations used in our study (if this release does not infringe the licences of the four NLG corpora under analysis, we will make the data available through an open data library like Zenodo).\n\nFinally, we did not think the paper required an ethical consideration section, but we have provided a detailed limitation section. We are willing to include any suggestions you might have for an ethical consideration section in the camera-ready version of the paper.", "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 is important and interesting research about NLG evaluation and addressing this problem adequately would lead to a beneficial impact within NLG criteria for evaluating language models.\n\nThis provides a valid tool for the NLG evaluation, as well as, adds value to NLG models' calibration.\n", "reasons_to_reject": "The uncertainty and variability terms seem to mislead the benefits of this study, and also reduce the paper’s readability. Another term that reduces the paper’s impact is “fitness” instead of “model fit”.\n\nExperimental results seem to benefit open-ended information as reported by the authors.\n\nThe authors did not mention the reproducibility of the work and if the code will be available for further research.\nEthical considerations are not highlighted within the paper.\n", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper tackles the problem of uncertainty in NLG models against human production variability. More specifically, uncertainty representation is the probability distribution over sequences of tokens from two mechanisms - an underlying statistical model and an interactive decoding algorithm. The uncertainty representation is analyzed in compliance with the variation of human production and categorized as aleatoric or irreducible uncertainty within the data generation process.\n\nThis study analyzes principal applications within NLG, such as text classification, machine translation, text simplification, and conversational AI.\n\nBased on the experimental results of the statistical fitness (calibration of models’ uncertainty), the authors conclude that NLG models tend to overestimate the production variability in open-ended tasks and the opposite in open-ended ones. \n", "excitement": 3, "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": "*Typos:*\n\n131 i.e. instead of i.a.\n\n148 add connector: and length)\n\n170 add space between words: strategies—yet \n\n171 add space between words: )—or\n\n404 missing comma for i.e., - overestimate variability, i.e.\n\n454 add space between punctuation: ).Upon\n\nFigures 12 and 13 have the same information. Also, please add to the caption what is presented in each figure.\n\n649 responses.\n\n658 remove purposes.\n\n659 remove purposes.\n\n664 humanly-acceptable?\n\n672 e.g.., \n\n675 correct reference to \"see Levinson (1983)\".\n\n\n*Readability suggestions:*\n\n144 (e.g., Yuan et al., 2021; Zhong et al., 2022). \n\n132-134. Instead of just referencing BARTscore, better adding BartScore (Yuan et al., 2021), so the reader would understand better, as it was done on lines \n\n163 change \"traction\". \n\n163 remove e.g.\n\n196 change given an input: about the output Y was given input X = x.\n\n203 change usually enough.\n\n210 remove the apostrophe from process'\n\n210 to 212 - please rephrase due to inputs, outputs.\n\n212 to 216 = rephrase.\n\nFootnote 2: rephrase - how it is hard to estimate and interpret for text generators.\n\nFigure 1: Remove the apostrophe of humans.\n\nFigure 1: add boldfaced examples in the text. Figure 1 is difficult to read due to the flow of the text. It is not clear which one is generated \nby DialoGPT.\n\n424 to 425: please rephrase.\n\nFootnote 6: Add italics to pmf and refer to the appendix section. Add \"that\" to which is all that our analysis tools require.\n\n430 to 431: \"handful of cases\" to \"a sample\".\n\n433. Change \"instead\", or possibly rephrase.\n\n448 to 454: change appreciate, rephrase paragraph.\n\n488 Change \"now\" or rephrase.\n\nFigure 9: Tuples that share \"the same\" color have different decoding parameters.\n\n511-521: Change the text for a table or a different form to improve readability.\n\n552-553: rephrase\n\n571-572, remove parentheses and change the underline for italics.\n\n587 change \"only a single generation\" for \"a single generation\". \n\n1131 because (a)\n\n1133 and (b)\n\nFigures 6 and 7 have the same caption.\n\nFigure 8 (higher means pairwise similarity).\n\nFigure 9 for tasks, probe, and decoding algorithm tuples.\n\nFigure 9 Tuples that share \"the same\" color.\n\nFigures 16 and 17 have the same caption.\n"}}, {"review_id": "1Xd8toZfrS", "reviewer": "Reviewer_hk61", "summary": "", "questions": "- Q1: What's the relationship between the cross-variability in eq3 and divergence metrics used in section 6.1 and 6.2? Why is the marginalized cross-variability not used directly as statistical divergence?\n- Q2: I could not fully grasp the part of \"variability of lack of situational grounding\" from line 561. Why is the context example in line 571-272 not available to model in prompt, but is available to human (I assume so)? Does it mean that the input information, i.e. prompts to both human and model, is different? Is the main point on model-human comparison or human-human comparison as mentioned in the beginning?", "limitations": "", "rating": 4, "confidence": 4, "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 your comments and questions.\n\n**W1 “selection of models”**  \nFor each of the four NLG tasks in our experiments, we have selected models that (1) are publicly available, (2) have been used in previous work _on the task_, (3) are conventionally accepted as good models _for the task_ and (4) are reasonably sized. For text simplification, we did not find any available trained model, so we used a versatile model like FlanT5. We agree it is important to include this explanation of the rationale used to select models and we will do so in the camera-ready.\n\nPlease also allow us to clarify that, with this paper, we are proposing a framework for probing the uncertainty of NLG models’ and evaluating it against human productions, and this contribution is intended to go beyond the experimental results obtained on our selection of models. We hope that our framework will be used by the community as an additional important evaluation criterion for NLG systems, especially to assess them in more open-ended tasks. In the camera-ready, we will also clarify the relative importance of the framework vs. the empirical results. \n\n**W2 “Different divergence metrics in 6.1 and 6.2 may impact conclusions”**   \nWe report results for all divergence metrics in Appendices D and F. These confirm that the trends we highlight in the main paper hold regardless of the specific divergence metric used. We mention this explicitly in Section 6.1 (lines 419-422) and more succinctly in Section 6.2 (lines 436-438). As you noted, we explain why we introduce each divergence metric (lines 398-407 and 433-436). We will use the additional page of the camera-ready to briefly discuss the nuanced differences in results and their interpretation obtained with mean difference vs. Wassterstein distance, which (as you can judge from the Appendix) do _not_ have a significant impact on our conclusions. \n\n**Q1 “Cross-variability in eq3 vs divergence metrics in section 6.1 & 6.2; why not use marginalized cross-variability directly as statistical divergence?”**  \nThank you for this thoughtful question. We tried to address this point in lines 374-386 but we will have another go here, hoping this clarifies our rationale. \n\nIt is certainly reasonable to evaluate a model based on what you call “marginalized cross-variability”. This is the interpretation exploited by most automatic NLG metrics: large positive cross-variability values indicate that model generations closely match the human references. \n\nHowever, in this paper, we do not take any single production as a ‘reference’ to be closely ‘matched’. This is because references can vary a lot with respect to each other (especially in open-ended task), as we try to point out throughout the paper and as we show empirically in Section 5. It is exactly this variability that we target in our analyses, and in particular, models’ ability to reproduce human-like levels thereof. \n\nSo, when we compute cross-variability following Eq. 3, we use the human responses not as ‘references’ to be ‘matched’ but as a set of plausible responses with which we estimate model variability. This complements our estimates of (self-)variability in Eq. 2. \n\nTo evaluate whether estimates of model variability (both obtained with Eq. 2 and Eq. 3) are in compliance with the variability observed in humans (Eq. 1), we use two metrics of statistical divergence.\n\n**Q2 \"lack of situational grounding?”**  \nThe WritingPrompts dataset is constructed from a Reddit forum where one user posts the beginning of a story (or a story prompt), and other users post continuations of that story (or responses to that story prompt). For example:\n\nPrompt: “You're a king and your kingdom is in the midst of rebellion. The issue is that you don't disagree with the rebellion.”\n\nResponse 1: “I’m the king. I should be kicking and crying and cursing the lands for this rebellion… but… I’m not mad. These are my people, and they have every right to be mad by my standing [...]”\n\nResponse 2 (*one hour later*): “I am the supreme sovereign. The highest power. The heart and soul of the realm. None dare question me, and all do as I say. Or at least that's the story we present to the. The reality is much less clear cut. In the real world [...]”\n\nResponse 3: …\n\netc.\n\nJust like the humans in this example, the NLG system in our experiments is conditioned on the prompt—and on the prompt only. \n\nNow to the example of \"variability of lack of situational grounding\" from line 561. The prompt for this instance is “all top level comments in this prompt take place in the same world, so make them all fit together”. That is, the user who proposes this prompt is asking other users to make use of each others’ responses (in particular, “top level comments”) to create stories that feature elements taken from multiple user responses. As a consequence, while the first few responses are likely only conditioned on the prompt (so quite open-ended), later responses will also contain elements from earlier user comments, which users can see (and are thus less open-ended). The NLG system, instead, can only see the prompt and therefore lacks the additional situational grounding that Reddit users can take into account.\n\n*\"Is the main point on model-human comparison or human-human comparison as mentioned in the beginning?\"* We evaluate model outputs against human responses. The human-human similarities are necessary to obtain an estimate of plausible variability to evaluate the models against.\n\n**P1 “variability or similarity”**   \nWe had many internal discussions about this point. We hoped variability would allow readers to more easily connect to and reason about uncertainty (high variability = high uncertainty). We do agree that the current presentation can be improved further, and will consider either changing the underlying similarity functions to distance functions, or renaming our production variability probes k(.) to production similarity probes.", "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": "- S1: The problem of uncertainty comparison between model and human is less explored, and I think the paper could spark more interest in this domain.\n- S2: The definition of production variability on the generated texts with statistical functions could serve as a new measurement to the NLG uncertainty/diversity.\n- S3: The paper is well-written and very easy to follow.", "reasons_to_reject": "- W1: The biggest problem to me is the the selection of NLG models for different tasks. For each task, a different model is picked without justifications of the reason and rationale of such selection. Furthermore, the same model is not evaluated across tasks, and the single model in each task is not compared with other alternatives at all. This could cause concerns on the reliability of results and conclusion.\n- W2: The divergence metrics are different in section 6.1 and 6.2. Although the authors provide explanations of the two, the switch of the metrics might imply that different metrics could have a considerable impact on the conclusion.", "questions_to_authors": "- Q1: What's the relationship between the cross-variability in eq3 and divergence metrics used in section 6.1 and 6.2? Why is the marginalized cross-variability not used directly as statistical divergence?\n- Q2: I could not fully grasp the part of \"variability of lack of situational grounding\" from line 561. Why is the context example in line 571-272 not available to model in prompt, but is available to human (I assume so)? Does it mean that the input information, i.e. prompts to both human and model, is different? Is the main point on model-human comparison or human-human comparison as mentioned in the beginning?", "paper_topic_and_main_contributions": "This work empirically assesses the production (output text) variability for between text generation model and human productions. The product variability is defined by different similarity functions in lexical, syntactic, and semantic levels. The paper considers the human variability as a form of aleatoric uncertainty, and compares model variability with human variability in divergence of such variability metrics.\nThe experiments are conducted on four NLG tasks with each having multiple references, and the results show that NLG models overestimate production variability in open-ended tasks and underestimate it in more constrained tasks. Popular decoding algorithms for generation model are demonstrated to have a similar effect on the difference between model and human production variability. Additionally, qualitative analysis on instance-level production variability is also complemented to gain insights on model variability.", "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": "- P1: I think it is very confusing for the definition of k(.,.) It is called production variability, but essentially a similarity metric. Therefore, higher value of k(., ,) indicates low variability, and vice versa. In multiple places, e.g. line 237-238 and Figure 2, the name and the values are misleading, and I suggest the authors could change it to a more consistent definition like (1 - similarity)."}}, {"review_id": "QhqgulIb0F", "reviewer": "Reviewer_mAJR", "summary": "", "questions": "Questions/Comments (line number)\n\nA (55) Should an NLG system capture the observed variability? Why? What’s wrong with an NLG system that gives, say, 5 excellent continuations to a prompt when humans can reliably come up with 10?\n\nB (112) What is “statistical fitness” in this context? Is this a technical term?\n\nC (186) What is a probability distribution over a production? Typically, LMs give probability distributions over strings. Is that what you mean here?\n\nD (197) For some previous work on how to estimate things like entropy from linguistic distributions, see Aurora et al., (2022) “Estimating the Entropy of Linguistic Distributions”\n\nE (209) My immediate first thought here was that you were going to look at length as one of the probe metrics. Why didn’t you look at length? It seems like a relevant factor along which production can vary.\n\nF (245) This is a relatively simple metric for looking at syntactic complexity. Did you consider others that take syntactic structure into account, such as, say Tree Edit Distance?\n\nG (436) I think it’s necessary to introduce the Wasserstein 1 Distance formally in the main body of the text, given that it’s not a well known measure of distance.", "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": "Thank you for so thoroughly engaging with our work!\n\nWe address your concerns next.\n\n(a) Thank you for your suggestion, we will add a condensed version of Appendix A to the main paper!\n\n(b) While we are very excited about the deeper connections between production variability and its sources, the format of a conference paper would not have allowed us to do them full justice. We have therefore decided to keep our contribution in this paper focused on the proposed statistical framework and on showcasing its potential for the evaluation and analysis of NLG systems (hence the submission to the “Interpretability, Interactivity, and Analysis of Models for NLP” track). We explore this connection further in two follow-up studies, which we look forward to referring to in the camera-ready of the paper.\n\n(c) Thanks for making this point, which we believe highlights an issue with the presentation of our quantitative results (we will remove phrases like “same ballpark” from the camera-ready) rather than with their substance. Indeed, the very reason for the introduction of human control groups (lines 439-466) was to be able to rigorously interpret (absolute) divergence scores relative to control divergence scores obtained by human held-out groups. We will consider expressing the relation between models’ and control groups’ divergence scores mathematically (e.g., through a ratio), though we are not sure this will make results more interpretable or informative.\n\nWe address your questions next, and will make sure the camera-ready reflects our answers.\n\n(A) In this paper, we evaluate NLG systems in terms of whether they are faithful models of human production, which is something that allows us to apply them to interesting psycholinguistic questions like the one hinted to in question-response (b). From a more applied NLP perspective, there is nothing inherently wrong with a system that shows higher or lower levels of variability than humans. In fact, for some applications (e.g., fact-based question answering), lower levels of variability than those observed in a population of humans may be desirable. Regardless of application-specific target levels of variability, our framework provides the technical tools to measure variability and compare it to those targets.\n\n(B) We used \"fitness\" to refer to a statistical model’s ability to *fit* or *reproduce* (characteristics of) a set of observations. We will use \"model fit\" instead.\n\n(C) Yes, for simplicity, we equated linguistic productions to their string representation.\n\n(D) We will include a mention to this interesting work in our camera-ready.\n\n(E) We have indeed used length difference as a similarity metric in preliminary experiments, but we decided to focus on our three interpretable metrics that target three classic levels of linguistic analysis. We hope that future work will investigate length differences as well as other dimensions of interest (see lines 678-685 in the Limitations section for further examples).\n\n(F) The space of possible similarity metrics is very large and exploring it thoroughly was beyond the scope of this paper. Syntactic distances can indeed be computed using metrics that capture structural differences between utterances in a more fine-grained manner (e.g., tree edit distance or difference in syntactic tree depth). Similarly, semantic distances can be computed with a more taxonomical approach (e.g., via WordNet synsets) or using NLI models to capture semantic equivalence (Kuhn et al. 2023, as cited in the paper); and distances between dialogue act types can be detected using dialogue act classifiers (Stasaski and Hearst, as cited in the paper). \n\nG) If space allows, we will expand and move our discussion of W1 in Appendix B to the main text.\n\n\n**Reproducibility**. We accidentally removed mention of code in our submission, but we will most definitely release the code required to replicate all our experiments. To reiterate our response to R1; in the camera-ready, we will include a link to a public repository with the code to reproduce all experiments in our paper. To further aid reproducibility, (1) we will upload our fine-tuned models to the Hugging Face model hub and (2) we are considering the additional release of the large dataset of model generations used in our study (if this release does not infringe the licences of the four NLG corpora under analysis, we will make the data available through an open data library like Zenodo). We hope this will change your thoughts on the reproducibility of our work.", "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": "Reasons to Accept:\n\n(a)  The topic of this paper is both interesting, understudied and (to me at least) timely.\n\n(b) The paper has two clear take-aways which will be relevant to the community: (a) underestimation of variability for closed tasks and overestimation for open tasks and (b) the effect of decoding strategies on the variability. \n", "reasons_to_reject": "Reasons to Reject:\n\n(a) The probing process could be justified better, and explained more clearly. In fact, I think a lot of good justification already exists in Appendix A. I would suggest moving this discussion into the main body of the text and moving Section 7 into an Appendix.\n\n(b) Overall, I think the authors could draw more connections to the psycholinguistic literature, especially to speech planning and production.\n\n(c) Section 6.2 is methodologically weaker than the other experiments in my opinion. The authors conclude that the decoding settings are close to the unbiased sampling which are all in the “same ballpark” as human scores. The authors need to figure out how to quantify this claim rigorously.\n\n", "questions_to_authors": "Questions/Comments (line number)\n\nA (55) Should an NLG system capture the observed variability? Why? What’s wrong with an NLG system that gives, say, 5 excellent continuations to a prompt when humans can reliably come up with 10?\n\nB (112) What is “statistical fitness” in this context? Is this a technical term?\n\nC (186) What is a probability distribution over a production? Typically, LMs give probability distributions over strings. Is that what you mean here?\n\nD (197) For some previous work on how to estimate things like entropy from linguistic distributions, see Aurora et al., (2022) “Estimating the Entropy of Linguistic Distributions”\n\nE (209) My immediate first thought here was that you were going to look at length as one of the probe metrics. Why didn’t you look at length? It seems like a relevant factor along which production can vary.\n\nF (245) This is a relatively simple metric for looking at syntactic complexity. Did you consider others that take syntactic structure into account, such as, say Tree Edit Distance?\n\nG (436) I think it’s necessary to introduce the Wasserstein 1 Distance formally in the main body of the text, given that it’s not a well known measure of distance.", "paper_topic_and_main_contributions": "Summary: The authors propose a method for quantifying the uncertainty of natural language generation systems and compare a number of current NLG systems against humans. They find that for open-ended tasks, models demonstrate more uncertainty than people, while this is reversed for more closed tasks, such as translation. They argue that decoders make models’ generation more human-like but that no decoder is stand-out compared to the others. Overall I thought this paper is on a very interesting subject, however the paper could be improved through more careful justification of their probe choices.", "excitement": 4, "reproducibility": 2, "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": "(102) I would mention locally typical sampling here. The whole point is to mimic human production (variability) so informed readers will be wondering about it at this point.\n\n(117) To me, it’s still unclear exactly what you did, at the end of the introduction. I would state these in more mechanistic terms during the intro, especially the probe itself.\n\n(308) This looks like entropy, which really threw me off for a second. I guess you use H for human here? I would suggest changing it.\n(359) “an text generator” → “a text generator”\n\n(Footnote 8 / Figure 5) I would mention the text of Footnote 8 in Figure 5. I was very confused about why there was no human data presented in this plot at first.\n\n(593) Overall, I think the discussion of aleatoric uncertainty can be dropped. This was mentioned briefly in the beginning but not given a formal treatment and I don’t think it adds much to the understanding of the results.\n"}}]

Author rebuttal:

Meta-review: The authors analyze how NLG models represent uncertainty. The authors argue that NLG systems should capture variation within human language production.For every generation prompt text, the authors compare properties of generations obtained through repeated model sampling, with multiple human productions— finding that uncertainty is lower when the task setting is more constrained. All authors appreciated the motivation of this work and recognize the need for improved calibration methods.
Decision: Accept-Main