On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation
Authors: Tiago Pimentel
Clara Isabel Meister
Ryan Cotterell
Venue: ICLR 2023 notable top 25% (2023)
Keywords: language generation
automatic evaluation
contextual embeddings

Abstract: A good automatic evaluation metric for language generation ideally correlates highly with human judgements of text quality.  Yet, there is a dearth of such metrics, which inhibits the rapid and efficient progress of language generators. One exception is  the recently proposed Mauve. In theory, Mauve measures an information-theoretic divergence between two probability distributions over strings: one representing the language generator under evaluation; the other representing the true natural language distribution. Mauve's authors argue that its success comes from the qualitative properties of their proposed divergence.  Yet in practice, as this divergence is uncomputable, Mauve approximates it by measuring the divergence between multinomial distributions over clusters instead, where cluster assignments are attained by grouping strings based on a pretrained language model's embeddings. As we show, however, this is not a tight approximation---in either theory or practice. This begs the question: why does Mauve work so well? In this work, we show that \mauve was right for the wrong reasons, and that its newly proposed divergence is not necessary for its high performance. In fact, classical divergences paired with its proposed cluster-based approximation may actually serve as better evaluation metrics. We finish the paper with a probing analysis; this analysis leads us to conclude that---by encoding syntactic- and coherence-level features of text, while ignoring surface-level features---such cluster-based approximations to string distributions may simply be better for evaluating state-of-the-art language generators.

Published as a conference paper at ICLR 2023
ON THE USEFULNESS OF EMBEDDINGS, CLUSTERS AND STRINGS FOR TEXT GENERATOR EVALUATION
Tiago Pimentel* Clara Meister*?? Ryan Cotterell?
1 University of Cambridge, 2 ETH Zürich
tp4720 cam. ac. uk, {clara. ryan. cyanecotte
ABSTRACT
A good automatic evaluation metric for language generation ideally correlates highly with human judgements of text quality. Yet, there dearth of such metries, which inhibits the rapid and efficient progress of language generators. One excep- tion is the recently proposed MAUVE. In theory, MAUVE measures an information- theoretic divergence between two probability distributions over strings: one rep- resenting the language generator under evaluation and the other representing the true natural language distribution. MAUVE's authors argue that its success comes from the qualitative properties of their proposed divergence. Yet in practice, as this divergence uncomputable, MAUVE approximates it by measuring the divergence between multinomial distributions over clusters instead, where cluster assignments are attained by grouping strings based on pre- tre-trained language model's embed- dings. As we show, however, this is not a tight approximation-in either theory practice. This begs the question: why does MAUVE work so well? In this work, we show that MAUVE was right for the wrong reasons, and that its newly proposed divergence not necessary for its high performance. fact, classical divergences paired with its proposed musstr-bassd approximation may actually serve as better evaluation metrics. We finish the paper with probing analysis; this analysis leads to conclude that-by encoding syntactic- and coherence-level features of text, while ignoring surface-level features- cluster-based substitutes to string dis- tributions may simply be better for evaluating state-of-the-art language generators
INTRODUCTION
Probabilistic text generators have improved greatly over the last years, with models producing increas- ingly human- like text (Yang et al. 2019; Brown et al., 2020; Raffel et al. 2020 Rac et al., [02TT] Hoff mann al. 2022 As the gap between human and model -eneratee text closes, the quality of our eval- uation metrics becomes ever more important for determining generator quality, especially given the increasing number of user-facing systems employing these generators. While human evaluations serve as the gold standard, they are costly (in both time and money), leading researchers to rely on automatic metrics i.e. metrics that can be measured by a computer- for the bulk of their development process.
Many automatic language generator evaluation metrics share the same underlying mechanism: the quantitative comparison of two probability distributions. Specifically, most metries measure difference between the distributions over strings defined by: (1) a language generation modef" and (2) the natural language itself. This includes some the most widely used language evaluation metrics 3
cross-entrope (Shannon] 1948), perplexity (Jelinek et al. [1777]. and (more recently) MAUVE (Pillutla
cet al. 2021). As typically applied to evaluate language generators, however, these metrics have number of computational and qualitative issues (discussed in ( Such issues manifest empirically:
the most commonly used automatic metrics are known to correlate poorly with human judgements
(Wiseman al. 2017
Reiter 2018 Sellam et al. 2020 Gehrmann et al. 2021).
"Equal contribution.
'Code available at httpps: : / thub. com/ ryco wwwp:laaaccclttesiiiaaaao.a www.uagegguggggeee ava vat on
We define language generator probability distribution Iw over strings Specifically, we consider this distribution as used during generation. F.g., , if decoding is performed with nucleus sampling, we consider the final distribution where every sentence with tokens not in the nucleus is assigned probability of O.
Most measures consider are metries in a strict sense; we the term "metric" out of convention.

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A newly proposed metric stands apart: MAUVE (Pillutla al. 2021). 1). In theory, MAUVE measures the area under the curve formed by the divergence between two probability distributions, qualitatively mimicking precision-recht
quantification (Djolonga al., 2020 Kynkäänniemi et al. 2019). The authors attribute the success of their metric to the qualitative properties of this new class of divergences. Yet, due to this divergence heing in practice uncomputable, PPlllutla et al. propose an approximation to it. Specifically, rather than directly comparing the original two distributions over strings, MAUVE first clusters samples taken from these distributions based on the embeddings of pre-trained language model; it then estimates the proposed divergence using the samples' empirically- observed multinomial distributions over cluster assignments. As we will show, this approximation is bad in both theory $ and practice $ the point that the term "approximation" is arguably a misnomer. Thus, the reasons why MAUVE works well-knowledge which is important for the continued progress of language generator evaluation metrics-ard are still unknown.
In this work, we aim to uncover these reasons. To this end, we consider the axes on which MAUVE differs from other evaluation metrics: is MAUVE's success due to the new divergence metric, to its "approximation", or both? Empirically, we identify MAUVE's substitution of probability distributions over strings with probability distributions success. We show that mathematically, this substitution leads to a quite biased estimator of the original string-based divergences. Yet it also leads to metries with lower variance and stronger correlations with human judgements. In fact, all divergence measures analysed here correlate more strongly with human judgements when cluster-based distributions are used in place of string- based ones.
embedding- -aasdd clusters as the main factor for its
Finally, in order to understand the root of the effectiveness of these cluster-based metrics, we probe the clusters themselves. We find that sentence-level permutations within texts noticeably affect cluster assignments, suggesting that cluster-based metrics are susceptible to attributes such as coherence. On the other hand, basic manipulations that render text unhuman-like like, such as removing all articles from the input text, do not seem to affect these metrics significantly. Together, these results lead us to conjecture that embedding- -based metrics may be favourable when estimating the quality of state-of-the-an (SOTA) language generators, as SOTA models are known to (at least typically) produce grammatical text. That is, by ignoring surface-level features text-while emphasising discourse- and coherence-level ones-clustered embeddings may simply be better suited for the evaluation of the top language generation systems. Yet these findings also suggest routes through which such metries can be gamed, bringing into question their robustness. We believe these findings, along with the theoretical framework we provide for evaluation metrics' comparison, are important for the further development of language generator evaluation metries.
DIVERGENCE METRICS FOR LANGUAGE GENERATOR EVALUATION
When evaluating language generation systems, we will first assume the existence of an unknown ground-truth distribution P.. This distribution is defined over strings w and its domain spans W Σr, where Σ is an alphabet of words and Σ* is its Kleene closure. Second, we are given probabilistic text generator q.. which is also distribution over W. An evaluation metric for language generator qu can now be defined as a measure of its "distance" from P.: A(p.. 9w). In short, A(.,)) should return high values if qu is bad approximation pe. and it should return low values if it is good one.
Notably, it is not clear whether 4. being good approximation P. in terms of an arbitrary A A(::) guarantees that it will be a good language generator. Indeed, models that perform well in terms of standard metrics, such as perplexity, often still produce poor-quality text (Holtzman al. 2020). Thus, we are interested specifically in A(; -) that correlate highly with human quality judgements.
More formally, define human quality judgements as a (potentially noisy) mapping (x from language generator to a real-valued score. For fixed p.. a useful metric (pp..)) for evaluating the quality of language generator 9N is one whose scores correlate highly with humanscores((). This notion can be operationalised as follows. Assume we have N language generator models. Let us define:
Junan (g
(N))
[ax (a(!))
,...., )
Sustric (g ..... (N) ^ (pm. A (pa. [cTo
(2)
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We then quantify metric's usefulness specific natural language task (and its distribution p.) as:
quality(A,Pu) |corr (Ōnunan 6 Snatric)
We now review common choices for (::-). Given the probabilistic nature of most language generators, a number of divergence measures are among these choices, which quantify the difference between two probability distributions The rest of this work focuses primarily on this class of metrics.
H(p {w), which is equivalent (up to an additive constant) the forward Kullback-Leibler (KL) divergence,
Forward Divergence. Cross-entropy, (p qw)
such choice:
(L (w) H( q.) H(pw) ② H(p q-)) = AH(P,
(p (w)
(4)
where
signify additive multiplicative equivalence. (1) is true since H(Pw) is constant with respect to qw- Since Pearson and Spearman correlations the metrics we use to evaluate A 'ss quality-arr are invariant translational shifts, the cross-entropy and forward KL are equivalent as language generator metrics. We will refer to them interchangeably during subsequent comparisons.
Backward Divergence. Albeit much less common, another potential evaluation metric would be the backward (exclusive) KL divergence:
A (pw. KL((g>) Pw) As opposed to the forward KL, when use as an evaluation metric, Eq. is not effectively equivalent the cross-entropy between 9ur and Pw. as (q..) is not constant across language generators gw-
(5)
Exponentiated Divergence. By far, the most common choice of A to evaluate language models is
the perplexity: Aperp(Pun (pwe)/) (w) = S() Notably, perplexity is equivalent (up to a multiplicative constant) exponentiated Kullback-Leibler divergence between Pw and qw. which follows from the same relationship as in Eq. (4). Given the property that both Pearson and Spearman correlations are invariant to a change in scale, the perplexity and exponentiated KL will thus be equivalent as language generator metrics. For consistency, we will use solely the exponentiated KT in our analyses:
Dexp (Dws (we) e
KT.(p l(w)
Jensen-Shune Chasnn-Shannon Divergence. Note that the KL divergence is non-symmetric and unbounded. the other hand, the Jensen-Shannon (JS) divergence- defined as the average of two KLs symmetric with respect to its inputs and is guaranteed to produce bounded values:
(Pw Iwo)
[KL(pw) || rr:)) + KL(g || rr))))
2 = Apw + A) Jw
Area Under the Curve (AUC) Divergence. Finally, information divergence frontiers
recently proposed class of metrics for generative models (Sajjadi et 2018 Kynkäänniemi et al.) 2019). The variant proposed by Pillutla et al. (202T) computes the area under the curve formed by a series of Kullback-Leibler divergences we change a mixing parameter X:
AAUC(Pw: )w) = - AUC (e (L(pwllrr) - - -=KL(gwllr))
x = Ap +
A) qm
(8)
where À is varied across the interval [0, 1), and Ry0 is strictly positive real-valued scaling constant. Note that we define the AUC divergence as AUC(.,) so that a larger value indicates greater discrepancy with the reference corpus Pw.
3 INFELICITIES AND APPROXIMATIONS
There are several issues, both computational and qualitative, with using the divergences presented in to evaluate language generators. We now review these issues, along with both commonly and newly-propose methods to address them via approximations.
'Here we make use of shifted divergences: a divergence measure that potentially has an additive constant, i.c., there exists constant E R such that ((.,)) :) + is divergence. For purposes, additive constant should not affect the quality of metrics, the correlation in Eq. (3) is translation-ivvarian.
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3.1 NECESSITY OF FULL SUPPORT
A well-known property of the (forward) KL divergence between two distributions Pw, and 9w is that is infinite for any 9w that assigns probability to an event in the support of Pw (i.e., for which Pw(w) > 0). The above is often not issue for Aex and A most neural language generators cannot assign probability to any string due to the final softmax operation typically used to project their outputs onto the probability simplex. However, these same models are often used with decoding strategies that prune the space W: e.g., both top-k and nucleus sampling modify qw such that strings which do not meet certain criterion are reassigned probability. While top-k and nucleus sampling typically lead to systems with qualitatively better text, they will likely be given an infinitely bad score by both Aeep and A _,, which is perhaps too harsh a penalty for an otherwise good language generator.
3.2 Pwe IS UNKNOWN
In practice, we do not have access to the true distribution pw- Rather, we are typically given corpus (wi") N In=1- whose instances we assume to be sampled i.i.d. from P The common approach to address this issue is thus derive statistical estimator A that uses this corpus to approximate A. There are two common strategies for building such estimators: Monte Carlo and plug-in estimation.
Monte Carlo Estimation. Our i.i.d. assumption w.r.t. samples in {wiw N [== allows us to derive a Monte Carlo estimator for certain divergences. We start with the forward KL divergence:
log Pwr (W
(T
del
log Ow gw (w!") + const N
N
qu (w(hi"))
where const E R is constant with respect to qw- Eq. is an unbiased estimator of KL divergence, which in turn allows us to build estimators A_, and Aexp. Unfortunately, unbiased estimates of A,,, is and AUc are not as straightforward to compute, as they require explicit knowledge of pa rather than just samples (see App.AA) Aj. This issue motivates the use of our next set of estimation techniques.
Plug-in Estimation. Here consider estimation via building approximation of p.. itself to use in the formulas given in $2 Specifically, we construct a density estimator for pa (which we denote Pw) and "plug it into a given A However, this is a bit circular: the task of building a language generator 9w itself is often framed as density estimation of p... Thus, we think q. is the "best" estimator for Pw, would be nonsensical; by the definition of a (shifted) divergence, it would always lead to the lowest possible value of A, e.g.. A (g (w) O. To plug-in estimation in this setting, we should therefore choose a different estimator for Pa. e.g., from a family of density estimators that differs from those used to create q... More formally, we consider a function which takes a corpus as input and produces a (queryable) distribution Pw T([w?"") N secondary model, e.g., an n-gram model or neural network, trained on the corpus {w?w N
should logically use it in our plug-in estimator. Yet, using 9.
This function typically induces a
Jn-i-
Our chosen may introduce biases (e.g., from the inductive biases of the architecture parameterising π) into metrics' estimation. To balance out such biases, may consider using the same method to create an approximation @w for use in our plug-in estimators, rather than directly querying qw:
A (f (PW N
In= (w.) (I
Pw)
(10)
Plug-in estimators for AIs and DAUG defined similarly. Further, if Fw is smoothed approximation to the original q.. using it may also mitigate the issues discussed in $3.1 We thus also compute estimators for the forward/exponentitted divergences using plug-in estimators, e.g..!
N
A (( N Jn=1 Owe
1
log Tw (wfw)
(11)
N
n=
'Often, plug-in and Monte Carlo estimators must be used together. Even if we measuring the divergence hetween two queryable distributions, the over W is infinite and non-dccomposable, thus uncomputable.
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Unfortunately, most functions cannot produce good estimate of Ps, using only small corpus, which is the case we consider since we rely evaluation sets for [w V.V /==1- While the best available language models are a class of x typically trained on millions (if not billions) of sentences, a standard evaluation set quite small-on the order of one ten thousand sentences-and we cannot expect to provide good Pw when fit using only such small dataset. Accordingly, depending on our choice of n, this class of metries may be either high variance or high bias, both of which are problematic.
3.3 CLUSTERING-BANE BASED APPROXIMATIONS
For the Â above that require density estimators for P- and/or qu choice of iT will have large effect on its value. We may thus wish to rethink our approximation technique altogether, and instead work with different distributions for which we can create lower variance density estimators. This is the approach used by Pillutla et al. (2021) when approximating A Auc. Specifically, instead of computing the above metrics the original distributions Pw and yw- they use the cluster-based distributions pe. and ge. Given a pre-trained language model, these cluster-based distributions are defined as:
Pe(c) = Σ (w)) 1 of (PLM[(w)))
(12)
WEW
where PLM(-) takes as input an utterance w and outputs an embedding r, and d(.) is a pretrained clustering funetion. Note that the function ((-) is trained jointly on samples from the two distributions under consideration as we will detail later; we defer the reader to /Pillutla et al.] (2021) for a more de-
tailed explanation of the procedure. Given these distributions, of all the divergences above, simply by substituting the original p.. and qn with the new Pe and gc-
can evaluate cluster-based versions
4 ANALYSING CLUSTER-BASER APPROXIMATIONS
We now take a closer look at the biases introduced by the substitution of cluster-based distributions suggested in $33 For simplicity, we foeus the bias introduced to (T |l [w) computation involved in MAUVE'S AAUC. This divergence can be decomposed as:
KL )
KL(p(c) ll (())) + KL(p(w c) ll g(w lc) Z
(L ll gc)
(13)
string based KL
cluster-besed KL
where (1) follows from the fact that p(c, w) p(w), which true because the cluster assignment is deterministie, i.e.: p(c| w) 1(c= (PLL((w)). See the full decomposition of this equation in App. B Notably, as KL divergences are always non-negative, the cluster-based version is negatively biased, lower-bounding the string-based one. Further, the actual measurement is done on the distribution over cluster assignments p(c): the distribution p(w c) is completely ignored.
Assuming a reasonable number of clusters is used when defining Pe. however, should be easier to create good approximations of distributions over clusters than distributions over strings due to the sheer difference in the size of the supports alone. Consequently, the variance of cluster- metrics should be lower, at the cost of the bias introduced by this substitution. Further, it is not clear whether this bias inherently bad when evaluating the quality of language generators: the answer to this question must be determined empirically (by measuring the correlation in Eq. (3). ). To this end, we now provide an empirical comparison between string- and muuserr-baeed language generation evaluation.
EXPERIMENTS
Setup. We follow the setup of Pillutla et al. throughout our experiments. We compare systems for open-ended text generation 4- with human-genenin generated text pw- As human- guman-generated samples, we use 5k strings taken from WebText's test set. As model-generated text, we sample 5k strings from each of our evaluated systems, conditioning our models on the first 10 words of humann-gnnratte strings before sampling (i.e., a cext-comlettio task). For our language generators, we compare model architectures (all variants of GPT-2), each under two decoding strategies, giving total of 8 systems. Explicitly, we compare the small. medium, large, and XL versions of GPT-2, decoding strings using either ancestral or nucleus sampling. Following Pillutla et al. (002).
nucleus
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probability of 0.9 for small and medium, while 0.95 for large and XL GPT-2's. Importantly we run our experiments only on English text, which is notable limitation of our work; future work should verify that findings hold across languages.
String-based Approximations Pw- To compute our string- based divergences, we require a sec- ondary language model Pw to estimate pw- Further, following the issues highlighted in $3 we will also rely on secondary language model Zw to estimate q.. We will use r-gram models for these approximations. Specifically, we use Kneser-Essen-Ney smoothed 5-gram models, as implemented in KenLM (Ney et al.) 1994; Heafield 2011). We choose n- n-gram models explicitly because-while they are by no means SOTA language models-thee they should have inductive biases which are different from the models are trying to evaluate. We present results using LSTM-basee estimators in App. When computing AAUC, we use a scaling constant of 0.2.
Cluster-based Approximations Pe- Cluster-based distributions, as presented in Eq. 12 are defined by choice of PLM(.) and pre-trained clustering function é(-). We rely GPT-2 XL. as our PLM, and use K-means as our clustering function; results using other pre-trained language models in App. B Specifically, we first extract embeddings from the last word in each sentence using GPT-2 XL and then use PCA to reduce their dimensionality (keeping 90% of the original variance explained); results using mean of word embeddings can be found in App. We then train K-means (with K = 500) on a joint set of GPT-2 embeddings extracted from: the 5% numan-generaan strings, and 5k model-generated sentences. Finally, approximate Pe. and &e by computing the frequency with which strings (among these 5k used ones) are assigned to each cluster. To avoid infinite divergence measures, estimate distributions using Laplace smoothing with 1 (which is equivalent to imposing a Dirichlet distributed prior with a = 1 over the cluster allocation). When computing A AUC, we use scaling constant of 5
5.1 DOES Pe APPROXIMATE P..
75-
Our first experiment tries to identify whether Pa and qe provide faithful approximations of p. and l-- To this
50-
Correlation
end, compare both Tw and To the true qu. i.e. the
25
language generator under evaluation. Explicitly, we com- pute the Spearman correlations between the probabilities assigned by each model to the strings (w J===
-5-
Gw
-50
small
nedium
large
xl
Fig. presents these correlations. We see that Mlat-despite being estimated on very little data-probability esti- mates from our 7--grmm models correlate strongly with the ground-truth probabilities of gw; this result holds for all four GPT-2 architectures. On the other hand, based probabilities consistently present negative correlations with g This result has an important implication: if cluster distributions do not correlate with qur then KL Te is likely a poor estimate of (L || (w)) This further implies that the approximation used by Pillutla et al.) is not an accurate estimate of AuUO g.), which brings into question whether this new divergence is really responsible for MAUVE'S success.
Model
Figure I: Correlations between the true qu and the estimated zo and Tw.
5.2 A AS TEXT EVALUATION METRICS
We now compare how various string- and cluster-based divergence measures correlate with human judgement scores R In short, Fig. 2 shows that all divergences do better when estimated with cluster distributions. These results evince that MAUVE'S (Pillutla et al.) (021) high correlations with human judgements (represented here as Au 9o)) are mainly due to their use of cluster- based approximations (pe, qc), rather than to their proposed divergence AAUC. In fact, we see
N 'We ran our entire pipeline (from sampling strings [w Ja=1 from to evaluating the KLs) with 5 different seeds. The variance across seeds is depicted as error bars in Figs. andd
7 We follow Pillutla et al. (2021) experimental setup here. Note that Pillutaa et al. provide an in-depth analysis of experimental design choices and show MAUVE is robust to various hyporparameter choices.
We use the human judgement scores collected by Pillutla al. 2021).

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100
Correlation (%0
Carrearion (%)
75
75-
75
Carrelarion
50-
50-
25
0-
9
AlP. Rc)
M. 9w)
AiP.. 9c)
AlPx.
AlP 9.1
Ai. 9p1
(a) Interestingness
(b) Sensibility
(c) Human likeness
Figure 2: Correlations between string- and cluster-based divergences with human judgement scores.
Legend: exp in dark green; A_ in orange; A in blue; Ass in pink; AUe in lime green.
slight improvements over ÂAUC when using the divergences Å and ÂJS instead. Furthermore, cluster-based divergences appear to more stable, exhibiting smaller variances across random seeds. Collectively, our results suggest that cluster-based divergences may produce better metrics of text quality than string- based divergences. This motivates the question: Which components of natural language are captured by cluster- buster-based distributions Pc, and which are overlooked by ignoring p(w when computing the cluster-based divergences? Our next set of experiments aim to answer this.
PROBING CLUSTERS
To better understand the aspects natural language that our cluster distributions encode, we must first understand how d(PLM(-)) partitions the string space W. In other words, we must understand what components of natural language-as e.g., semantics, syntactic attributes, or surface features- lead to strings being assigned to similar or different clusters. Such an analysis should provide deeper insight into the actual similarity being measured by cluster-based divergences (while also revealing how such metric might be gamed). To this end, we probe (Alain & Bengio 2016) the clusters learned by the MAUVE algorithm for a number of linguistic attributes -ncluding subject matter, sentiment, prose style, word order, basic grammaticality and document length-looking at how they affect both cluster assignment and the divergence scores between texts that differ in these characteristics. Notably, we probe cluster assignments Our probing analyses are thus exempt from most recent criticism against probing methodologies
wrihhoyywwihhut relying any diagnostic classifiers (Adi et al. (017).
(Hewitt & Liang 2019 Pimentel et 2020a Ravichander et al. 2021 Elazar et al. 2021
6.1 FINDING FEATURES Pe ENCODES
Setup. We look at texts annotated with different attribute categories in order to explore correlations between the presence of these attributes and cluster membership. Specifically, we analyse texts': sentiment, authorship, and topic (using the Yelp Polarity, News Category, and 20 NewsGroup datasets, respectively). Further details on datasets are provided in App. D For each of these classification datasets, we compute the cluster- cluster-category distributions that result from the MAUVE algorithm using the standard training split for the respective datasets; all evaluations are then performed on test splits. Explicitly, first learn partitioning <(.) of the embedding space (w.r.t language model PLM(-)). Each cluster is then labelled with the majority category represented in that cluster by training examples; text categories in the test set then predicted using this labelling, depending which of the clusters the example falls into. For comparison's 's sake, we use four language models as PLM(-): GPT-2 with small 1, medium um, large, and XL architectures. Results using embeddings from BERT Devlin et al. 2019) can be found in App. Further, we use two methods for learning clusters:
((.) Learned on WebText. Using the same procedure as in $5.2 we train PCA and K-means functions to partition the embedding space (again relying on WebText's 's test set for our data). This mimics the setting under which partitions would be learned in practice
((.) Learned on Training Set. We instead train the PCA and K-means clustering functions on the analysed dataset's 'ss training set. This setting studies the partitioning that clustering functions have the capacity to learn an ideal setting, i.e., where the attribute in question one of the main differentiating factors between texts.
If strings the training set assigned cluster, we lahel with the overall majority category.
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WebText
Training Set
WebText
Training Set
Web Test
Training Set
0.8
0.25
Accuracy
0.7-
Accuracy
0.20
Accuracc
0.2
GPT-2
Small Medium Lange
0.15
0.6
0.10
XI.
0.5
20
20
49
+b 40:g0 -59gp
Number of Clusters
Number of Clusters
Numher of Clusters
(a) Sentiment
(b) Authorship
(c) Topic
Accuracy when predicting different attributes of text from their cluster assignments. Assign- ments (i.e. ((-)) are learned using text from either WebText, or the training set of the respective classi- fication datasets. Dashed lines represent baseline accuracies, i.e., always guessing the majority class.
Figure 3:
Results. In Fig. Sa we see that, least for large numbers of clusters, cluster assignment is indeed indicative of a text s sentiment. Interestingly, this is the case even when clusters are trained on data that not particularly polar sentiment (i.e., on WebText). On the other hand, we are only able to predict author and topic (with reasonable accuracy) when clusters are learned on text data with authorship and topic as distinguishing factors. These results indicate that, while writing style and subject matter are captured by the text embeddings, they likely were not being used as distinguishing features between corpora in our cluster-based divergences. We further see that, in all classification settings, the capacity to encode these analysed attributes appears to increase with model size, perhaps suggesting that the embedding spaces of larger models decompose along higher-level features of text.
6.2 How TEXT FEATURES IMPACT
We next assess how changing different features of our evaluated text impacts divergence scores. Specifically, we look at the impact of: text truncation; article removal; stopword removal; sentence- level permutations; and word-level permutations.
Setup. We follow similar setup to $ primarily consider human-gent generrted text these experiments (i.e., the 5k human-writeen articles in WebText's test set). We take the first 2500 articles of this dataset as our reference corpus {w]" ===1" We then use the remaining 2500 reference strings as the comparison corpus, i.e., in place of the model-generated text that we would typically evaluate {w% J Jn=1 In order to explore how changing specific features of text affects A w.r.t. the reference corpus, we then compute scores when making the following modifications to the comparison corpus:
In order to create a more controlled setting, though, we
No modification (p). This is baseline experiment where we keep the original strings unchanged.
Text Truncation (Pshort). We truncate texts to 1/3 of their original length. This allows to understand whether the divergence metrics pick up on differences in dataset length statistics.
Article Removal (Pno art)). We remove all articles ('a", 'an' and 'the") in the text. This allows us to understand whether the divergence metrics can distinguish between texts with or without basic levels fluency and grammaticality.
Stopwords Removal (Pno stup). We remove all stopwords (e.g., 'that' or 'so') ) in the text. This allows us to understand whether the divergence metrics can detect differing levels of syntactic coherence, rather than just focusing on content words 10
Sentence-level Permutation (Pswap). We permute the first halves of texts (as delineated by sentences) across the entire corpus (i.c. randomly reassigning the strings' first halves). This allows us understand whether the divergence metrics detect coherence.
Word-level Permutation (Prand). We randomly permute all words in a text. This allows un- derstand whether the divergence metrics can only distinguish between bag-of-word level features. GPT-2 Baseline (g). As an extra baseline, we also use the first 2500 generations from GPT-2 XI. the WebText text-completion task, as in 5
Stoowordds are defined common words, such "that" or "so", that primarily serve a syntactic function. We the of English stopwords defined by NLTK (Bird et al. 2009..
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Comparison
Forwand
Backward
Exponetrianzl
15
AUC
Text
1.00
75-
0.6
Porn
3-
50-
0.4
0.2
0.25
Pam
01
0.0
0.00
Pe
130
250500
50 100
250.00
30U 250500
250.500
30
250500
Number of Clusters
Figure 4: Divergence measures between two corpora: the reference text is unmodified while the comparison text undergoes perturbation. Higher values indicate a greater discrepancy according to A.
Results. Fig. shows that certain alterations to the evaluated text-such as completely removing have almost no impact on its divergences from the reference corpora for various A. In fact, text without any articles is judged as better than GPT-2 XL's 's by all of the cluster-based divergences (see Fig. 10 for zoomed-in version). Further, while this perturbation undoubtedly affects the text's fluency, it has less of an effect on A than, e.g., truncating texts. This is arguably undesirable: A metric of text quality should place more emphasis on fluency than surface statistics, such as length.
On the other hand, our metrics deem text with stopwords removed as utterly different from the reference. Permuting words within texts has similar effect, demonstrating that, at least to some extent, the embedding space captures notions of syntax and grammaticality, rather than pure unigram statistics. The increase in A shown when performing sentence-level permutations likewise suggests that the clusters delineate different levels of coherence to some extent. In Fig. 13 (in App. D. we perform additional experiment where we again probe the clusters (as in $ but for surface features of text this time, such as the percentage of stopwords and punctuation symbols in text. There we see evidence that such features of text are not strongly encoded in the clustering scheme.
Perhaps surprisingly. when applied to cluster-based distributions, all of the studied A metrics rank the distance of the perturbed texts from the reference texts in the exact same order (this is clear in Fig. TO zoomed-in version of Fig. 4. For these perturbations, the A differ only in the magnitude of their outputs, further suggesting that the ÂuUC metric itself likely not critical for the effectiveness of MAUVE. One potential avenue for future research would be investigating whether different algorithms for discretisation of the embedding space create clusters that align with specific linguistic attributes: this could be useful diagnostic for language generators' improvement.
This section's 's results-alorg with those of $6.1 Suggest that divergences based on PLM embeddings more sensitive syntax- and coherence-reiated properties of the target text than to its superficial features. The opposite, however, might be said of our string- based distributions. These findings offer a potential explanation for the effectiveness of metries that make use of PLM embeddings, such as MAUVE or BERTSCORE (Zhang et al.] (222)). As current SOTA language generators already typically produce grammatical text, being invariant surface statistics may perhaps be feature- as opposed to a bug-when trying to assess the quality of the text they produce. Yet, this may also reveal potential ways which such metries can be gamed, bringing this paradigm's 's robustness into question.
7 CONCLUSION
In this paper, we analyse MAUVE, recently-proposed automatic metric for language generator evaluation. While MAUVE correlates quite well with human quality judgements, it is unclear which of the metric's design choices are in fact responsible for its success-a shortcoming that impedes the further development of language generator evaluation metrics. We attempt to rectify this shortcoming. We first provide a general theoretical framework for the comparison of language generator evaluation metrics. Then through series of empirical studies, we identify MAUVE'S substitution of probability distributions over embedding- based clusters- place of the traditional distributions over strings as the attribute largely responsible for the metric's success. In order to better understand the nature of this improvement, we probe the clusters used by the density estimators, analysing what they ignore and what they emphasise about the input text. We find that, while distributions over clusters are sensitive to syntax- or coherence-leve perturbations to the text, this is not the case for several surface- level perturbations. We thus conjecture that, by focusing evaluation metrics may simply be better suited to rank high- begghherformin models, and that this is a general paradigm worth further exploration.
higher-level text features, cluster- cluster-based based
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8 ACKNOWLEDGEMENTS
We would like to thank the authors of MAUVE for sharing their human evaluation data with us. We would also like thank Gábor Melis for his insightful feedback on initial version of this paper, our anonymous reviewers for their help in improving our final draft, and Luca Malagutti for detailed feedback on clarity and presentation.
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A A MONTE CARLO ESTIMATOR FOR BACKWARD, JS, AND AUC DIVERGENCES
Consider Monte Carlo estimator for A.:
N A (Pw. gw) =o (L pl = Σ
1
log Iw g (w 9w
(14)
N
Pa Pw(ww (wi..)
n=1
where w ~ 4w- We can easily sample from 9w However, computing Eq. (14 requires knowledge
of log Pw- which we do not have. Thus we resort to other techniques for estimating these divergences, as discussed in $
B
STRING VS. CLUSTER-BASED KULLBACK-LEIBLER DECOMPOSITION
The decomposition in Eq.
13
can be shown as follows:
(L ([g) Σ p(w)
log P( w) ((w)
(15)
WEW
K
plc, w) log p(c)p(w c)
Σ
g(c) g(w](w]]) cc)
WEW
log p(c) ((c)
+ log p(w c)
Σ plc, w)
<w c)
WEW
K
plc) {(c)
K
log p(w w
Σ p(c) log
Σ p(w (w c)p(c)
<(w
WEW
pp(,,w)
Marginalise over all
K
Σp(c) log p(c)
+ Exp
Σ p(w c) log
p(w c)
((c)
<(w c)
LWW
KL(p(c) |l ((e)) + KL(p(w
c) g(w c))
20
KL(pe ge)
where again (1) follows from the fact that p(c, w) = -(ww,, which is true because the cluster assignment is deterministic, i.e.:
p(c w) = =c)
(PLLM(W))
(16)
Thus, KL(p(c) 4(()) provides biased estimate of KL qw))
C RELATED WORK
Over the years, a number of evaluation metrics have been proposed for language generation tasks (such as translation and summarization); the most well-established and commonly used include BLEU (Papineni et al., 2002). ROUGE (Lin) 2004) and METEOR (Banerjee & Lavie 2005). However, these metrics- which rely on n-gram statistics-have been shown to correlate poorly with human judgement (Reiter) 2018). Recent metrics have improved upon these correlations with more advanced techniques, e.g., BEER (taanoevvćć & Sima an 2014), MoverScore (Zhao et al. 2019) and BLEURT (Sellam al. 2020). These metrics, though, are intended for language generation tasks with strict set of reference texts. While reasonably effective for directed generation tasks, they do not transfer well to the open-ended domain.
For tasks in which there not a clear reference, e.g., story generation, basic statistics are typically employed to provide preliminary evaluation of generated text. Such statistics include n-gram
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repetitions (Welleck et al., 2020). Zipfian coefficient (Holtzman al. 2020), the perplexity of generated text (Fan et al., 2018). Final assessments of language generation systems are still often performed using human evaluations, automatic metries on their own have not proven sufficient for differentiation between top-end language generation systems.
Automatic evaluation metrics for language models based statistical divergences have been proposed by a number of different authors. For instance, MMesserr & Cotterell (2021) assessed the quality of language models while using a number of divergences between distributions over surface statistics in text corpora. Xiang et al. (2021) propose the approximation of distributions over strings with distributions in the embedding space in standard divergence metrics. Pilluda et al. (202I) present MAUVE-the object of study of this work work-which is based on new divergence metric inspired by the information frontier metric. [Pillutla et al. (2021) also propose a new way to approximate it using clusters over word embeddings. We provide an analysis of this paradigm, showing that practice, we should expect this method to provide poor estimation of the intended quantity. We go perform empirical experiments to identify the proposed use of distributions over clusters itself is likely responsible for the metric's success, rather than characteristics of the new divergence. We see this work as complementary to Pillutla et al. (202I), providing deeper and more comprehensive insights into the metric's inner workings.
Srtrgennes(Djolongee et al. (2020) Besides proposing this AUC divergence
D EXPERIMENTAL SETUP
Embedding Models. To obtain text embeddings for input strings, use several different PLM. Namely, we use the four sizes of GPT-2 (Radford et al.] 2019), as well as BERT-base and BERT-large (both cased: Devlin et al. 2019). For the former, we use the embedding of the final token. For the latter, we use the embedding associated with the special CLS token. In both cases, we additionally present results using the average across token embeddings in App. E All texts are truncated 512 tokens (for experiments in 662 this truncation is performed before any manipulations of the text), in order to ensure that all models can process the input text in their context.
For our probing analysis in 66 we employ the following datasets:
Sentiment. To analyse sentiment, we use Yelp Polarity (Zhang et al., 2013 dataset extracted from the Yelp Dataset Challenge 2015, which contains binary sentiment classifications for highly polar Yelp reviews. We use 10k: examples randomly sampled from the training set and 5 examples randomly sampled from the test set.
Authorship. analyse authorship, we use News Category (Misra Grover, 2021 Misra 2022 a dataset consisting of 200k news headlines from the years 2012-2018 obtained from HuffPost. We scrape entire articles from the URLs provided by the dataset. We only use the subset of articles for which the article's 's author has 400 articles within the dataset, giving training set of 32k and a test set of 6k with 46 unique authors.
Topic. To analyse topic, (partitioned into train and test sets using the original splits) on 20 topics, such as subcategories of science, politics and religion. The distribution over text topics is relatively uniform.
the 20 NewsGroup dataset, which contains 18k newsgroups posts
"SSpecifically, Djolonga et al. proposed a framework that measures the trade-off hetween precision and recall using Rényi divergences.
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E ADDITIONAL EXPERIMENTS
Corrtation (60)
50
25
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Al0 Ge)
AlP, 4e) GP mediun
Ale de) GPT-2 Large
Alp GP12 xl
Alp.. de) BURT base
Alpc. BERT large
Alpw Oul Aegram
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Figure 5: Correlations between string- and cluster-based divergences and human judgement scores using a number of estimators (with different PLM(.) when defining Pe, or language models for pw). Legend: Aepp in dark green; A_, in orange; A_ in blue; Ajs pink; AUC in lime green.
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Figure 6: Correlations between cluster-based divergences and human judgement scores using number of PLM(.) define Pe- In this figure, we use the average embedding per sentence produced by a PLM(-) to compute Pe. as opposed to the final embedding. Legend: Aexp in dark green; A., in orange; A. in blue; Ais in pink; AAUG in lime green.
16

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Published as a conference paper at ICLR 2023
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Figure 7: Accuracy when predicting different attributes of text from their cluster assignments. Same
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Figure 8: ÁAUC scores between reference text and alternate text distributions as function of number of clusters used to estimate Pe and qe- Importantly, in this figure, we compare the first 2500 sentences ()) in the human-generated WebText test set to these same strings, but under the proposed interventions. Iee., for all points corresponding to (altered) distribution (1), estimate our cluster distributions on the same set of human-generated sentences, but where the strings in one group have been intervened on. The baseline distribution p2 still represents the final 2500 sentences in WebText (as in the original plot); and baseline distribution is text sampled from GPT-2 XL.
17

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Published as a conference paper at ICLR 2023
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Figure 9: A scores between reference text and alternate text distributions as function of number of clusters used to estimate P. and q.. Results shown for embeddings produced using multiple LMs
18

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Published as a conference paper at ICLR 2023
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Figure 10: Zoomed in version of Fig manipulated in different ways.
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Figure 11: Version of Fig. clusters.
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Figure 12: Version of Fig. clusters.
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19

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Published as a conference paper at ICLR 2023
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Figure 13: R² when using cluster assignments predict % of tokens in text that are either punctuation stopwords. Setup follows that of $6.1 albeit using solely the WebText dataset to train our clustering functions in this setting. We compute the average percentage of stopwords or punctuation per cluster in half of our strings and use these pre-computed averages when predicting the percentages in the other half, computing this prediction's R2 (i.e. the percentage explained variance). We that larger PLMs which often claimed to provide better representations of language- do encode more information about such surface features than smaller models. This could simply be due to the fact that the embeddings from larger PLMs are typically of a larger dimension and, thus, have the capacity to encode additional (perhaps "less critical") attributes text. While these attributes do not appear be differentiating factors when partitioning the embedding space into a small number of clusters, they become relevant when partitioning into larger number of clusters. Even with several clusters and large PLMs, though, the 1e values find are still quite small, around 0.20.
20


Reviews:
[{"review_id": "smjKPEu72O", "reviewer": "Reviewer_mdcX", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The current paper focuses on revisiting one of the state-of-the-art NLG evaluation metrics, namely MAUVE. This paper proves that the good performance of MAUVE is coming from the cluster-based approximation it used but not the AUC divergence. Additionally, this paper conducts a probing experiment to understand what linguistic information has each cluster-based evaluator learnt.", "summary_of_the_review": "The paper is generally is in a very good shape and definitely should be accepted, but there are still some minor issues that are yet to be clarified or explained.", "strength_and_weaknesses": "Strength:\n1. This paper revisits one of the STOA NLG evaluators either theoretically or empirically. This explains which component of MAUVE really works and helps the future development of NLG evaluation research.\n2. Probing experiment helps readers understand MAUVE better.\n\nWeaknesses:\nI found no major risk in accepting the present paper. I have only one minor concern: I somewhat feel the probing results are not in line with the correlation analysis. Concretely, from figure 4, it appears that different divergence measures are sensitive to different linguistic information. For example, Exponentialed is sensitive to p_{rand} while AUC is sensitive to p_{swap}. However, they all have similar correlations with human scores. My understanding is that this is might be a result of the limitation of the human scores you used. More specifically, the probing experiments target syntax and coherency related information, while human scores in Figures 4 (i.e., interestingness, sensibility, and humanlikeness) are all from pragmatic aspects. More explanation is welcome here.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "POxGlDFY4Wa", "reviewer": "Reviewer_H8v4", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper is an analysis of Maude metric which computes the closeness of distribution of human-written text with the distribution of texts generated by generative models using the AUC divergence. In this paper, it is shown that the success of Maude is not because of the AUC divergence rather it is due to the cluster-based approximations of the original distributions using pre-trained LM embeddings. \n\nAuthors show that different divergences based on cluster-based approximations have positive impact on the metric's performance. They do a comprehensive probing on these cluster-based approximations and find out that many syntactic and coherence level features such as sentences-substitutions have higher impact on the cluster assignment of strings rather than surface-level features. These can be good features for evaluating SOTA generative models that mostly generate grammatically correct text while texts are not always syntactically correct and coherent.", "summary_of_the_review": "In overall the motivation, research problem and conducted analysis are written quite fluently, however there are some not clear parts that have been mentioned in the weakness section. ", "strength_and_weaknesses": "The comprehensive analysis of different divergences and estimations of probabilities for various models with different sizes is the major strength of the paper that gives value to such type of analysis-based papers. The order of discussed contents in the paper looks very neat and clear to me specifically for the first three sections.\n\nThere are some minor issues in the experimental section, that are summarized here:\n\nIn string-based approximations, only ngram-based model has been considered, while to have a complete analysis it would be good to consider other type of generative models such as BART even though its decoder is GPT but still can be informative and good for comparison.\n\nIn section 5.1, authors have shown that cluster-based approximations are not accurate estimators for original distribution, thus the question that arises here is that why in section 5.2 it is shown that even though they are not accurate using them have good impact on different divergences and consequently the metric's performance. Some further explanations can be beneficial.\n\nIn section 5.2, different string-based and cluster-based approximations have been compared, the Monte Carlo estimations for specific divergences (forward, exp)  can also be useful to be involved.\n\nOne interesting experiment that can be added to the paper is the comparison between the inductive bias that can be added by different approximations for divergences to show the pros and cons of each approximations for the divergences.\n\nIn cluster-based approximations only last word embeddings are leveraged, is there any reasons behind it.\n\nI strongly suggest to use shapes for plots to be readable in any modes.\n\nBased on results in Figure 2, is there any probing of why substituting string-based approximations with cluster-based approximations in AUC divergence results in lower correlation enhancement specifically in interestingness evaluation of texts.\n\nTwo motivation questions in section 5 is not very intuitive. Some explanation can better lead the reader to the next experiments.", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}, {"review_id": "F-d6EplwxgW", "reviewer": "Reviewer_X1r9", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "The paper is about evaluation of open-ended generation of LMs, in particular the Mauve (pillutla et al.) metric.\n \nEvaluation of open ended text generation takes a distributional format. It is framed in this paper Divergence(p_w, q_w). This task has two sub-tasks, (1) Density estimation of p_w and q_w (when necessary). (2) calculating a distribution divergence metric of choice and -if necessary- approximate p_w or q_w to calculate this divergence metric tractably.  \n\n\nThe paper starts with laying a common formalism to compare these divergence metrics and notably revisits the AUC divergence metric used in the Mauve metric. This metric is intractable to calculate in practice and therefore P_true and P_model are replaced by multinomial probability over clusters of sentence embeddings using an external encoder. \n\nThis paper is a critique of the Mauve metric both theoretically and empirically, mostly the replacement of p_w and q_w with its clustering based approximation:\n- Section 4: it shows that the approximation kl(p_w,q_w) to kl(p_c,q_c) is biased\n- Section 5.1: it shows empirically that string based density estimations (using basic n-gram lm) of p_w and q_w correlations better with original data distribution than their cluster based counter parts\n- Section 5.2: Despite the results shown in section 5.1, authors show that divergence metrics correlate *correlate better with human judgements* when cluster based density estimation is used as an approximation. \n- Section 6: by probing the clusters authors show that clustering based on sentence embeddings (the core component of the clustering based density estimation approx of mauve) are more oriented to capture global features such as sentiment, authorship and topic and less sensitive to surface modification which is probably why they correlates with human judgements overall since most recent language models don’t suffer from disfluency,  although this could be a way to game the mauve score\n", "summary_of_the_review": "The paper overall serves as a good critique of the mauve score, empirical and theoretical findings in the paper seems to be solid, however the overall scope of the paper is quite narrow and focuses only on critiquing one paper. ", "strength_and_weaknesses": "Strengths:\n- The paper is well written the related work is quite educational, instead of listing papers authors make the effort of laying a theoretical ground for comparing difference divergence metrics and ways to approximate their intractability.\n- The paper shows theoretical and empirical drawbacks of the mauve score, and show why it does work in practice \n- I find the findings from section 5.1 and 5.2 to be interesting, mainly that methods that correlate with the real probability distributions $\\hat{p}_w$ and $\\hat{q}_w$ are not necessarily what is best to correlate with Human judgements $\\hat{p}_c, \\$hat{q}_c. This opens more research questions and could have made this paper have a wider scope.  \n\nWeaknesses:\n\nThe scope of the paper is quite narrow, and solely serves as a critique to the Mauve score paper which was published last year and yet not so widely adopted in LM evaluation. It is not clear how those conclusions could inspire future works for language model evaluation. \n\n\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "CHOmVqyZUO", "reviewer": "Reviewer_X7E3", "summary": "", "questions": "", "limitations": "", "rating": 8, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "This paper analyzes the performance of the recently-introduced and influential MAUVE metric for evaluating the quality of automatically-generated text by comparing it to human-generated text at the distribution level. It begins by setting out the rationale for the MAUVE approach, starting with difficulties in applying standard divergence measures to this particular evaluation problem and motivating the use of surrogate distributions such as MAUVE’s embedding-cluster-based multinomials. It then demonstrates that these surrogates provide a biased approximation to the true KL divergence (an ingredient in the AUC divergence used by MAUVE).\n\nExperiments show that cluster-based surrogates nevertheless have much better correlation with human judgments than do ngram LMs. This finding is independent of the particular divergence metric used, implying that the use of clusters rather than AUC divergence is the key factor in MAUVE’s performance. Further experiments probe the nature of the clusters, finding that they correspond to sentiment, but only weakly to style and topic. Finally, when comparing perturbed and natural human samples using the cluster approach, surface features such as articles and punctuation are found to be largely irrelevant.", "summary_of_the_review": "The paper is technically very strong, and it presents important experimental results in the key area of quality evaluation for text generation by LLMs. The insights into how MAUVE works need to be publicized.", "strength_and_weaknesses": "Strengths:\n\n1. Very well organized and clearly presented.\n\n2. The step-by-step rationale for the MAUVE approach (and variants), although not novel, is extremely thorough and provides useful structure for organizing work in this area.\n\n3. The finding about the central utility of LM-derived clusters is very significant, given the prominence of the MAUVE paper. This is something that needs to be publicized, and that will stimulate further research in a crucial area.\n\n4. The experiments into the nature of the clusters yield interesting insights, particularly that metrics like MAUVE are relatively insensitive to surface features, and might be vulnerable to gaming as a result.\n\nWeaknesses:\n\nThe evaluation is performed only with variants of GPT-2. It would be interesting to see whether the results carry over to more recent models such as GPT-3 or PaLM.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works.", "empirical_novelty_and_significance": "4: The contributions are significant, and do not exist in prior works."}, "extra_text": {}}]

Author rebuttal:

Decision: Accept: notable-top-25%