We're Afraid Language Models Aren't Modeling Ambiguity
Authors: Alisa Liu
Zhaofeng Wu
Julian Michael
Alane Suhr
Peter West
Alexander Koller
Swabha Swayamdipta
Noah A. Smith
Yejin Choi
Venue: EMNLP 2023 Main (2023)
Keywords: evaluation
semantics
ambiguity
TL;DR: By collecting an expert-annotated dataset of ambiguous sentences with disambiguations, we present the first evaluation of whether pretrained LMs can understand ambiguity in text, finding that task remains extremely challenging for all models studied

Abstract: Ambiguity is an intrinsic feature of natural language. Managing ambiguity is a key part of human language understanding, allowing us to anticipate misunderstanding as communicators and revise our interpretations as listeners. As language models are increasingly employed as dialogue interfaces and writing aids, handling ambiguous language is critical to their success. We capture ambiguity in a sentence through its effect on entailment relations with another sentence, and collect AmbiEnt, a linguist-annotated benchmark of 1,645 examples with diverse kinds of ambiguity. We design a suite of tests based on AmbiEnt, presenting the first evaluation of pretrained LMs to recognize ambiguity and disentangle possible meanings. We find that the task remains extremely challenging, including for GPT-4, whose generated disambiguations are considered correct only 32% of the time in crowdworker evaluation, compared to 90% for disambiguations in our dataset. Finally, to illustrate the value of ambiguity-sensitive tools, we show that a multilabel NLI model can flag political claims in the wild that are misleading due to ambiguity. We encourage the field to rediscover the importance of ambiguity for NLP.

We're Afraid Language Models Aren't Modeling Ambiguity
Alisa Liu &
Zhaofeng Wu
Julian Michael Alane Suhr +A Peter West
Alexander Koller** Swabha Swayamdipta o Noah A. Smith
Yejin Choi
Paul G. Allen School of Computer Science & Engineering, University of Washington UC Berkeley
Allen Institute for AI
Universsty of Southern California
Saarland University
New York University
Massachusetts Institute of Technology
alisaliuecs, washington edu
Abstract
The cat was lost after leaving the house.
LOST
unable to find
unable to be found
Ambiguity is an intrinsic feature of natural lan- guage. Managing ambiguity is key part of huran language understanding, allowing us to anticipate misunderstanding as communica- tors and revise our interpretations as listeners. As language models are increasingly employed as dialogue interfaces and writing aids, han- dling ambiguous language is critical to their success. We capture ambiguity in a sentence through its effect on entailment relations with another sentence, and collect AMBIENT," linguist-annotaeed benchmark of 1,645 exam- ples with diverse kinds of ambiguity. We design a suite of tests based on AMBIENT, presenting the first evaluation of pretrained LMss to recog- nize ambiguity and disentangle possible mean- ings. We find that the task remains extremely challenging. including for GPT-4 whose gen- erated disambiguations are considered correct only 32% of the time in crowdworker evalu- ation, compared to 90% for disambiguations in our dataset. Finally, to illustrate the value of ambiguity-sensitive tools, we show that a multilabel NLI model can flag political claims in the wild that are misleading due ambigu- ity. We encourage the field to rediscover the importance of ambiguity for NLP.
its way
neatral
The cat could not find its way.
Since took office, Wisconsin new has the 2nd highest health care ranking country.
SorWia Seen goareed comce
now, in contrast with before
currently, regardless of what before
+
entais
neutral
Wisconsin's health care ranling changed.
Figure 1: Ambiguity can be the result of innocent mis- communication (top), or deliberately used mislead one's listeners (bottom). For instance, cat may be lost in the sense of being confused about its whereabouts (entailment edge), or lost in the sense that others cannot find it (neutral edge). Each example in AMBIENT con- tains a set labels corresponding to plausible readings, along with disambiguating rewrite for each reading.
as communicators, we anticipate the possibility of misunderstanding: as listeners, ask clarifying questions, disambiguate meanings the basis of wide range of contextual factors, and backtrack and revise our earlier interpretations as needed. Beyond unintended miscommunication, ambiguity also an effective tool for sending covert messages, e.g., out of politeness or to mislead one's listeners while avoiding accountability (see Figure 1).
1 Introduction
Ambiguity seems to be an essential, in- dispensable element for the transfer of froormation.from one place to another by words. Thomas (1974), as referenced in the epilogue of Grosz (1977)
As language models (LMs) are increasingly em- ployed to act as dialogue agents (OpenAI, 2022; Shuster et al., 2022) or to aid human communica- tion as writing aids (Lee et al., 2022), being able to work with ambiguous language will make them more effective. This skill would support adaptation to different contexts, clearer communication, and identification of mislcading or deceptive language. Yet, the ability of pretrained LMs recognize am- biguity and disentangle possible meanings remains unstudied, partly because ambiguous instances are systematically excluded in the curation of bench- marks (Beigman Klebanov and Beigman, 2009).
Ambiguity is an intrinsic feature of language, al- lowing speakers to balance efficiency and clarity in communication (Zipf, 1949; Piantadosi et al., 2012). Language understanding thus requires rec- ognizing the presence of multiple interpretations:
'Data and code can be found at https: //tp::////ttubb com/ al alisawuf wetpwwfflls//ae fles/ ambient

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We present AMBIENT. Ambiguity in Entailment, an English benchmark of 1,645 examples covering a variety of lexical, syntactic, and pragmatic ambiguities, and more broadly sentences which can be plausibly read as conveying one of multiple different messages. Formally characterizing ambiguity requires choice of meaning representation to distinguish between possible interpretations, and enumerating the full set of interpretations can be tricky or impractical." 2 Thus, we adopt a functional approach: using the natural language inference (NLI) task format, we characterize ambiguity in the premise and/or hypothesis by its effect on entailment relations. 3 Each AMBIENT example consists of a premise and hypothesis pair, assigned a set of labels (among entailment, neutral, and contradiction), along with disambiguating rewrites corresponding to each la- bel when multiple are plausible (see Table for examples). Examples are collected through two ap- proaches: manual curation to target textbook ambi- guities, and expert annotation of automatically gen- erated unlabeled examples uncover more diverse phenomena. Through analysis, we find that crowd- workers can reliably distinguish different readings an ambiguous sentence and their impact on en- tailment choices; thus we can explicitly charac- terize the underlying reasons for uncertainty that would otherwise surface "disagreement" ($3).
task is much more challenging when formulated account for ambiguity.
Finally, to illustrate the value of ambiguity- sensitive tools, we present a case study of how multilabel NLI model can be used to detect mis- leading political claims in the wild. We find that the strongest model from $5, despite its limitations, can not only recover claims flagged by fact-checkers ambiguous, but highlight previously unidentified ambiguous claims, indicating the promise of such tools to aid real-world communication.
The simplifying assumption that text has only one interpretation has facilitated the development of large- benchmarks, yet limits the depth of what these benchmarks can evaluate. In this work we show that sensitivity ambiguity- funda- mental aspect of human language understanding- is lacking in our ever-larger models, and illustrate the value such understanding could bring.
AMBIENT
Traditionally, the NLI task requires predicting whether premise entails, contradicts, ir neutral with respect to hypothesis. Yet, ambiguities the premise and/or hypothesis (as in Table 1) may impact the determination of the label.
We present AMBIENT, dataset of 1,645 NLI examples, each annotated with ser of labels, reflecting potentially multiple readings of the premise and/or hypothesis. Ambiguous examples, i.e., those having more than one label, make up 35.2% of the datasct and include a disambiguating rewrite corresponding to each label; unambiguous examples have single label. The inclusion of un- ambiguous examples facilitates evaluating model abilities to first detect the presence of relevant ambi- guity, and then resolve it to distinct interpretations.
We design a suite of tests based on AMBIENT to investigate the extent to which understanding of ambiguity is acquired during pretraining of large LMs ($4). These tests evaluate whether LMs can di- rectly produce relevant disambiguations, recognize possible interpretations, and model different inter- pretations in their continuation distributions. We find that these tasks remain extremely challenging. including for the recent GPT-4 (OpenAI, 2023).
We use two approaches to collect source exam- ples: manual curation and automatic generation. Manual curation ($2.1) involves crafting a small set of examples targeting specific types of ambiguity. Further, to cover more diverse forms of ambiguity, produce a larger collection of examples via text generation and heuristic filtering ($2.2), followed by expert manual annotation ($2.3), forming the bulk of AMBIENT. Details are in $A.
Therefore, additionally investigate whether LMS can be finetuned on existing NLI data for the less demanding task of ambiguity recognition, with- out explicit disambiguation ($5). We adapt several finetuned NLI models to a multilabel setting, and find that the best model predicts the exact label set in only 43.6% of instances, suggesting that the NLI
FFor example, Koller al. (2008) find that including all possible quantifier scope readings in the Rondane Tree- bank (Copestake and Flickinger, 2000) results in 5% of tences having 2650,000 possible semantic analyses.
2.1 Curated Examples
The authors curate a set of 142 examples, which are either handwritten or sourced from existing NLI datasets and linguistics textbooks (Kearns, 2000;
'By design, we only consider ambiguities in NLI examples that affect the determination of the label, to provide natural setting where ambiguities are contextually relevant.

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Example
Disambiguation
Disambiguation
Type
P: I'm afruid the cal was hit by car.
Prugmatic (44.8 8%
P: I'm worried... NEUTRAL A:
P: I'm sorry share that... CONTRADICT C]
H: The cat was not hit by car. (NEUTRAL, CONTRADICT] 2:: [C
P: John und Anna are married.
P: hoth married.
married each CONTRADICT & 19 CJ
Lexical (20.0%%
H: John and Anna are not couple.
NEUTRAL [7 EE
(NEUTRAL, C
[5 N,
P: This seminar is full now, but interesting seminars ure being offered next quarter too.
There will be more seminars that are interesting. ENTAIL
There will seminars... that are more interesting.
Syntuctic (8.6 6%
H: There will be interesting seminars...
[] E]
NEUTRAL : [9 Nj
(ENTAIL, NEUTRALJ 2: 2
The novel has been banned in many schools be- cause its explicit language. H: The novel has not been banned in many schools. [NEUTRAL, CONTRADICT] Re: [4 N, 5 C]
There are many schools It is not the case that where the novel has not been banned.
novel has been bunned in many schools. CONTRADICT []
Scopul (7.6%)
NEUTRAL
[9 N]
P: is currently March, and they plan to schedule their wedding for next December. They plan to schedule... next year.
Coreference (2.90 9%
P: for December next year. ENTAIL
P:... for the coming December. CONTRADICT S cJ
[]
[ENTAIL, CONTRADICT :: [3 2,,
P: is difficult believe that the author of such masterpiece could have been only 23 years H: The author of the masterpiece was only 23. NEUTRAL)
Figurative (1 (1.
P: It is shocking that... ENTAIL [9E]
P: It is questionable that... NEUTRAL
N]
NEUTRAL 2: 6]]
new study has found nearly half all Americans are favor of gun control.
... that exactly half of all Americans... CONTRADICT
H: that about half of all Americans...
Other Hessss
H: The study found that half of all Americans favor gun control. {ENTAIL, CONTRADICT [1 2 6
[8 N
ENTAIL 2 [9
Table I: Ambiguous examples in AMBIENT with annouitt- GOLD LABELSJ. As analysis, we collect the : [distribution of NLI labels] judged by nine crowdworkers under the traditional single-label annotation scheme ($3), finding that disagreement on ambiguous examples is largely resolved disambiguations. The Type column indicates ambiguity type for each example, along with estimated representation in the dataset ($2.5).
Carnie, 2013). We choose examples ad hoc from the synthetic NLI datasets DistNLI (Ban et al., 2022) for predicate distributivity (c.g., "Sam and Frank gave a talk" may either mean separately or jointly) and IMPPRES (Jeretic al., 2020) for implicatures. We also include some instances with differing pragmatic and literal readings from NLI Diagnostics (Wang et al., 201 8), and ones leading to disagreement from large-scale NLI datasets like MNLI (Williams et al., 2018) and WANLI (Liu et al., 2022), The authors directly annotate these examples with the set of labels and disambiguations (examples in $A.1).
to be ambiguous. Inspired by WANLI (Liu et al., 2022), automatically identify groups of premise- hypothesis pairs that share reasoning pattern, to encourage the creation of new examples with the same pattern. We use WANLI as our source of examples; each group contains a randomly chosen example on which its two annotators disagreed (in- dicating possible ambiguity), along with its 4 near- est neighbors according to the final-layer embed- ding of WaNLI-trained NLI model. We observe that these groups can share interpretable ambigu- ity patterns, such sentences about the past (e.g., "When I was young, was obsessed") inducing a cancellable implicature about the present (that "7" am no longer obsessed; full prompt SA).
2.2 Generated Examples
To cover more ambiguities, we use overgenera- tion and filtering to automatically create a large corpus of unlabeled NLI examples that are likely
These groups of examples are formatted into a prompt with the instruction, "Write pairs of sen- tences that are related to each other the same

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Neutral
Write pairs of sente ices...
to say. H: it's hard to know.
Entailment
In-oomest examples from WANUJ
Pr: It's hard to know,
682
233
330
Py:
speak.
CON
Pr: It's
trath of the matter is,
1h
Pa it's hard to speak about
Total 1645
Contradiction
hard to determine.
NEU
Pa/lt's hard to articulate.
Figure 3: Distribution of set labels in AMBIENT.
CON
ther annotator discarded an example; the validators may additionally discard examples themselves.
Figure 2: Pipeline for the annotation of generated exam- ples in AMBIENT. Unlabeled examples created by InstructGPT, then annotated independently by two lin- guists, whose annotations are consolidated by an author.
Linguists annotate total of 2,616 examples. Due the option for discarding, 2,020 examples emerge from the annotation phase, and after valida- tion, there are total of 1,503 final examples.
way." For each prompt, sample 5 continuations from InstructGPT (Ouyang et al., 2022), discard- ing those that cannot be parsed into a premise and hypothesis.
2.4 Agreement
To calculate inter-annotator agreement for valida- tion, the four validators annotate a subset of 50 ex- amples in common. The Fleiss x agreement score on the binary classification task for each label is 0.62 for contradiction, 0.65 for entailment, and 0.44 for neutral, thus ranging from "moderate" to "substantial" agreement.
To further filter for likely-ambiguous instances, we use multilabel RoBERTa-large model trained on WANLI and retain all examples where the model assigns probability 0.05 to more than one NLI label, indicating at least slight uncertainty in whether there can be multiple possible readings.
2.5 AMBIENT Statistics
2.3 Annotation and Validation
The final dataset, which combines curated and ther-ntddthee---anntaeeddd examples, consists of 1,645 examples. We sample 100 for a develop- ment set and treat the rest as the test set. The label distribution is shown in Figure 3.
Examples acquired in $2.2 consist of unlabeled premise-bypothesis pairs, which we next annotate with label sets and relevant disambiguations. Fol- lowing AMBIGQA (Min et al., 2020) and as shown in Figure 2, each example is first annotated by two experts, then presented third expert for valida- tion and consolidation.
To understand the types of ambiguity present in A AMBIENT, the authors annotate random subset of 100 ambiguous examples with the ambiguity type, among lexical, syntacttic,ggurravive pragmatic, sco- pal, coreference, and other (described in $A.6). Results are shown in the Type column of Table
We recruit 37 university-level linguistics stu- dents for the annotation phase," as identifying am- biguities of sentence then delincating its possible interpretations is a challenging task. They select set of labels for each example, including the single- ton set when the example is unambiguous; when more than one label is chosen, they provide disam- biguating rewrite for each one. They are asked to discard the example if offensive or low-quality due to issues in fluency or coherence.
Does Ambiguity Explain Disagreement?
We conduct an analysis to understand how anno- tators behave on ambiguous input, under the tradi- tional 3-way annotation scheme for NLI. We find that ambiguity is recognizable to individual work- and explains much of the label variation that emerges, thus challenging the popular assumption that example uncertainty should be modeled as "dis- agreement" among annotators.
The validation phase is performed by a subset of the authors to ensure high quality (details in $A.4).
The authors review the two sets of annotations to revise and aggregate them into a single coherent an- notation, optionally adding interpretations missed by both annotators. Validation is skipped when ei-
3.1 Setup
We recruit crowdworkers on Amazon Mechanical Turk to review ambiguous examples in AMBIENT.
We refer to them as "linguists" elsewhere.

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Each example is reviewed by 9 workers. The task is split into three steps, each appearing only after the earlier steps are complete.
set of ambiguities (step (ii) and verifies their la- bels (step (iii). In this sense, the agreement rate is 89.7%. This points to the quality of the dataset and is used as a reference point for later experiments. Overall, input ambiguity is indeed source of "disagreement" in NLI under a single-label annota- tion scheme. However, we have shown that indi- vidual annotators overwhelmingly can recognize multiple possible readings of the input and their corresponding output labels, and much of this dis- agreement can be resolved in practice by incorpo- rating disambiguation into the task. In this way, input ambiguity can be disentangled from annota- tor subjectivity.
(i) Annotation of ambiguous example Follow- ing the traditional NLI labeling setup, crowdwork- ers are presented with the original ambiguous ex- ample alone, and asked to choose a single label.
(ii) Recognition of disambiguations The am- biguous sentence of the example (either the premise 3 hypothesis) is isolated for consideration." Three candidate interpretations are presented in a random order, composed of the two disambiguations and (In the case where an example has three interpretations, no dis- tractor is included.) Workers are asked to indicate whether each sentence is a "possible interpretation" of the isolated sentence. We instruct that this is sub- jective, and they should use their best judgment.
a semantically similar "distractor".
Evaluating Pretrained Language Models
In our experiments, we investigate the extent to which understanding of ambiguity is acquired dur- ing the course of pretraining. Our three tests eval- uate if LMs can directly generate relevant disam- biguations ($4.1), recognize the validity of plausi- ble interpretations ($4.2), and finally, model open- ended continuations reflecting different interpreta- tions ($4.3). For these tests, we consider only the ambiguous instances in AMBIENT.
The distractor ensures that workers do not con- sider all sentences as valid readings. and obtained by bock-traanslttnng the ambiguous sentence with Yorùbá using NLLB (Meta, 2022). A low-resource language is chosen so that the back-translation is a close, but often not entirely faithful, paraphrase.
(iii) Annotation of disambiguated examples Three new NLI examples are obtained by substitut- ing the ambiguous sentence of the original example with each candidate interpretation from (ii). Work- ers select a single NLI label for each new example.
As our set of LMs, we evaluate LLaMa (65B; Tou- vron et al., 2023) and GPT-3 (davinci), well as instruction-tuned models FLAN-T5 (xx1; Chung et al., 2022), InstructGPT (text-davinci-003 ChatGPT (gpt-3. 3. 5 5-turbo), and the recent GPT-4.
3.2 Results
4.1 Generating Disambiguations
As hypothesized, the original ambiguous examples produce high disagreement, with a Fleiss k score of 0.1 considered "slight" agreement (step (i)). Dis- agreement is largely resolved on the corresponding disambiguated examples (step (iii), with k increas- ing 0.67, representing "substantial" agreement. Moreover, annotators overwhelmingly recognize disambiguations as plausible interpretations of the ambiguous sentence (step (ii). True disambigua- tions are marked plausible 96.7% of the time, com- pared to 46.7% for the distractor. On average. 93.7% of annotators accept all true interpretations, thus recognizing the full set of possibilities.
We first study whether LMS can learn in-context to directly generate disambiguations and correspond- ing labels. We construct a natural prompt (see Table 2) by explaining that there is some ambiguity that makes the correctness of a "claim" (hypothesis) difficult to resolve given the "context" (premise). For each test instance, we randomly sample 4 other test instances as in-context examples.
As there are multiple ways to express the same disambiguation, we perform both automatic and human evaluation. For the former, we match each generated disambiguation with a reference disam- biguation based on the generated label. 6 Following A AMBIGQA, we score generations using the EDIT- F1 metric, which represents disambiguation by
Through this experiment, we additionally estab- lish crowdworker agreement with AMBIENT as the rate at which the majority vote recognizes the full
"If the label verbalizer for a disambiguation does not cor- respond to any lahel in the reference label set, then the model receives a score of O for that disambiguation.
For simplicity, we only include examples where either the premise or the hypothesis is ambiguous (93.1% % of examples).

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its added and deleted unigrams, and computes the Fl score between the reference and the prediction. For human evaluation, we use the same setup as the crowdworker experiment in $3 on 50 randomly sampled examples, except without step (i). We use three workers per example, and consider the LM correct example if the majority vote indicates that each disambiguation is plausible (step (ii)) and selects the model model-predicted NLI labels (step (iii)). Crowdworkers are not informed that disambigua- tions are monel-ggnerated.
Instruction
In each example, you will he given some context and a claim, where the correctness claim is affected by some ambiguity context. Enumerate context that lead to different judgments about the claim.
interpretations of the
Context: {prenise)
Claim: [hypothesis] Given the context alone, is this claim
frue. falss,, or inconclusive?
Example
We don't t know, because the context be interpreted in many
different ways:
(disanbiguaatin 13 Then the claim is true.
(di sambiguiioon 2) Then the claim false.
doiisabbguation 3) the cluim inconclusive.
Table 2: Few-shot template for the task of generating disambiguations ($4.1) when the premise is ambigu- ous. The label verbalizer correspondences are true + ENTAIL, false CONTRADICT, and inconclusive e- NEUTRAL. The instruction is stated once, followed by four in-context examples. At the end of the prompt, the test example is provided up until "1.".
Results Shown in Table 4, the best model is GPT-4 achieving an EDIT-F1 score of 18.0% and human-judged correctness of 32.0%. The latter can be directly compared to crowdworker agreement with AMBIENT itself 89.7% ($3).
One strategy for attempting disambiguation observe across models is restating the ambiguous sentence with additional context that directly af- firms or negates the hypothesis, rather than making a targeted revision to clarify the ambiguity. In some cases, this "shortcut" does lead to technically cor- rect disambiguations (and marked as such in human evaluation). For instance, for
Template
Correct Answer
(a) This may mean: (d)
True
[a] This does not necessarily mean: [d]
True
{a} This cannot mean: {d}
False
[a] This only mean: [d)
False
Table 3: Templates for True/False evaluation ($4.2).
where [a} denotes the ambiguous sentence and {d} a possible disambiguation. Given the infilled template followed by "True or pected to choose the correct answer.
P: He always ignores his mother's 's advice to follow his own dreams. H: He follows his dreams.
the LM is ex-
ChatGPT disambiguates the premise by restating it, followed by "and therefore does follow his dreams" versus "and therefore does not follow his dreams." The former forces the interpretation that he ignores her advice in order to follow his dreams; the latter the interpretation that his mother's advice is for him to follow his dreams. Thus, the human-judged correctness may overestimate the models' ability to precisely report the source of ambiguity.
execute this task zero-shot as the prompt template completely determines the label.
Results The T/F Acc. column of Table 4 shows the accuracy averaged across the four templates. The best model (GPT-4) achieves only 63.0% com- pared to the random accuracy of 50%, with other models ranging between 49.6% and 57.7%. When consider the proportion of disambiguations for which GPT-4 answers all four templates correctly, performance drops to 2.5%, below random guess- ing of 6.25%. We do not observe consistent trends across models on the per-template accuracy (shown in SC.2), though four of six models achieve the highest accuracy on template 1.
4.2 Recognizing Disambiguations
For the next test, we focus on the ambiguous sen- tences alone (without the rest of the NLI example). and create series of templated true and false state- ments about possible interpretations shown in both true that an ambigu-
Table 3. For instance, ous sentence may mean a particular interpretation, but also that it does not necessarily mean it. We consider the model prediction to be the token with the greater logit between True and False.? We
Furthermore, we observe that LMs are not inter- nally consistent across the questions. For example, for 76% of pairs of disambiguations (d1, dg) for the same ambiguous sentence a, GPT-4 both ac- knowledges that a may mean dj and may mean d2 (template 1), yet also asserts that a can only mean dj and can only mean de (template 4).
As the API for ChatGPT and GPT-4 does not return token logits, we simply consider whether the top-1 token is correct. We find either True or False is the top token in 97. and 99.7% of examples, respectively, indicating the task is clear.

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Correct (human)
KL Rank.
2017), e.g., replacing "schoof" with "library."
EDIT-F1
T/F Ace.
Acc.
We expect the LM model continuations from both disambiguations d, better than those from the
FLAN-T5
5.2
0.0
56.4
81.0
LLaMa
10.0
10.0
55.0
68.9
distractor d, i.e., for all true disambiguations d,,
GPT-3
10.1
2.0
57.8
75.7
InstructGPT
14.5
4.0
49.6
71.4
D(P( d) II P( a)) D(P(- d;) II P(- a)).
ChatGPT
13.0
18.0
57.7
GPT-4
18.0
32.0
63.0
We call the fraction of ambiguous contexts for which this is true the KL ranking accuracy.
Table 4: Performance of pretrained models on AMBIENT. Higher values are better for all metrics. A baseline that reproduces the ambiguous sentence as its disambiguation would achieve O EDIT-FI and human- judged correctness; random performance for T/F accu- racy is 50% and for KI. ranking accuracy is 32.8%.
Results The KL Rank. Acc. column of Ta- ble 4 shows that FLAN-T5 demonstrates the correct preference of continuations for 81.0% of exam- ples, making it the best model here despite its poor performance in other settings. The inconsistent trends suggest that results heavily dependent how competence on ambiguity is operationalized. Nonetheless, ambiguity remains a severe challenge across models and across the suite of tests.
4.3 Modeling Interpretation-Speciii Continuations
Finally, we determine whether LMs, when con- ditioned on an ambiguous sentence, implicitly model different interpretations their distributions of text continuations. Since LMs are trained to model words given context, understanding ambi- guity should mean recognizing the union of the contexts for sentence's interpretations.
5 Evaluating Multilabel NLI Models
Given that language models still struggle to pro- cess ambiguity in $4, we next investigate the effec- tiveness of finetuning them existing NLI data collected in the line of work on underspecificatiio and subjectivity in NLL. Here, consider the discriminative task of multilabel NLI prediction,
To measure this, we obtain continuations for each interpretation, and quantify how "surprised" the LM is to see them when conditioned on the ambiguous sentence." Specifically, we first sample 100 continuations e P(- d,) conditioned on each disambiguation d; as context. Then, we com- pare the likelihood of under the ambiguous sen- tence versus the corresponding disambiguation d, by computing log ((c d+) log P(c a). This describes how much the LM "suffers" by seeing the ambiguous instead of the unambiguous context," and is an unbiased estimate of the KL divergence between P(- d,) and P(- a) (proof in SC.3):
across both ambiguous and unambiguous examples in AMBIENT. Experimental details are in SD.
5.1 Methods
We experiment with methods that predict single probability value, a distribution over labels, or a set of labels. We use the development set of AMBIENT to tune threshold(s) that map the output of these models onto set of labels (see $D.1). All models are based on rober taberta-large, and we report results over 5 random seeds for model training.
D(P(- 44) 11 P(. a))
N
P(c d;) log P(e, [a)
Σ
Regression models We train a regression model on Uncertain NLI (UNLI; Chen et al., 2020) that predicts a value on [o, 1] representing the probabil- ity of the hypothesis being true given the premise.
= lim N-0
3=1
C -P((-)))
Intuitively, we the KL divergence not be too large - the LM should reasonably expect to see continuations for either interpretation. To quantify this, we introduce a "distractor" sentence formed by replacing a randomly selected noun in with same-category word from ConceptNet (Speer et al.,
Distributional models Distributional models aim to predict the distribution of annotator judg- ments. We use two models from prior work: 1) one trained on AmbiNLI (Meissner et al., 2021), with examples with multiple annotations from SNLI (Bowman et al., 2015) and MNLI, and 2)
"We exclude ChatGPT and GPT-4 from evaluation as the API does enable calculating likelihood under the model. "This method assumes that the likelihood of continuation is hased on its meaning alone, but surface-form attrihutes like style are confounding factor. See further discussion in $C.3.
The size of AMBIENT is not large enough for a train- ing split; future efforts to annotate data in the fashion of AMBIENT might he able to address this issue.

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Method and Train Set
EM
Macro F1
Group EM
6 Case Study: Detecting Misleading Political Claims
Molti. Dist. Reg
Uncertain NIJ (C+20)
24.5g.s
62.2pa
4.72.
AmbiNLI (M-21)
21.0g.
63.8na
10.12.
We illustrate the value of mumbigutty-sessitive mod- els via case study in detecting misleading political claims in the wild. Here, we the key insight that for ambiguous sentences, some paraphrases are naturally disambiguating, as paraphrases must either preserve the ambiguity or paraphrase par- ticular interpretation. Therefore, if we cast a given sentence as the premise and a paraphrase as the hypothesis, a multilabel NLI model predicting two or more labels should indicate the presence of am- biguity. Moreover, the paraphrase resulting in this prediction should reveal the source of ambiguity. We experimentally evaluate this idea on the de- velopment set of CLAIMDECOMP (Chen al., 2022), which contains 200 claims with their Poli- tiFact fact-checks. The authors read each instance and mark whether the fact-check describes an issue of ambiguity or factuality (regardless of whether we perceive ambiguity ourselves). Then we para- phrase each claim 5 times with InstructGPT zero- shot, and apply the multilabel WANLI model from $5, which achieved the highest F1 score, on each resulting NLI example. A claim is considered ambiguous if the model predicts more than one label for any paraphrase. Examples in Table 6.
SNLI MNLI (Z+22)
21.31.1
68.0n.1
4.71.
Multi-label MNLI (JD222
15.8a.4
63.2ns
0.91.
Multi-label WANLI (L+22))
35.1a.
72.5ua
19. 14.
Classifier over sets WANLI
43.6as
70.7pa
37.80
Table 5: Performance of multilabel NLI models on AMBIENT. While all model ouiputs are mapped onto a label set, their original output is one of regression (reg.). distributional (dist.), or multilahel (multi.) output. EM and Macro F1 measure performance the original example; group EM considers performance on both the original example and its disambiguations. We report the mean and standard deviation over 5 seeds.
a model trained through distribution distillation (Zhou et al., 2022), where a teacher model trained on SNLI + MNLI is used to re-annotate the data with soft labels then used to train new model.
Multilabel models Prior work trained a multil- abel model (Jiang and de Marneffe, 2022) on the development sets of MNLI + ChaosNLI by turn- ing distributional labels into discrete ones with threshold of 0.2. In addition, we train multilahel model on WANLI's train set (which has two anno- tations per example), as well as a elassifier over sets which performs 7-way classification over the power set of NLI labels, minus the empty set.
This method recalls 88.8% of ambiguous claims. While precision is lower at 12.4%, qualitative in- spection of false positives reveals many ambigui- ties that were left unmentioned in the fact-check, illustrating the potential of these tools to anticipate sources of misunderstanding. Ultimately, our anal- ysis suggests that fact-checking as a more general problem may need refinement, due to the possible presence of both true and false interpretations. This case study shows only one use case of ambiguity- sensitive models, and we hope for AMBIENT for benchmark further progress on this front.
5.2 Metrics
On the original examples, we caleulate the macro Fl score and the exact match accuracy (EM); the latter requires the model exactly predict the label set. We also report the group EM accuracy as the fraction of examples where the model exactly pre- dicts the label set for both the original NLI example and all of its disambiguations.
5.3 Results
As shown in Table 5, the multilabel model trained on WANLI achieves the highest macro Fl score of 72.5%, and the classifier over sets achieves the best EM accuracy of 43.6% and group EM accuracy of 37.8%. While the EM accuracy is substantially higher than the random- guessing baseline of 1/7 14.3%, it is considerably short of 89.7%, the rate which crowdworkers correctly predict the set of labels when presented with possible disam- biguations ($3). Overall, finetuning NLI models on existing data with label variation still leaves large room for improvement on the multilabel NLI task.
7 Related Work
Ambiguity Ambiguity is a longstanding and well-studied issue for NLP tasks involving sym- bolic analyses of sentences, such as syntactic and semantic parsing (Church and Patil, 1982; Koller et al., 2008) coreference resolution (Poesio and Artstein, 2005). However, as the field has recently shifted toward higher-level understanding and rea- soning problems, ambiguity in language has been largely overlooked.
In the space of moma-domain question-answering

---

Explanation of ambi- guity (ours)
Political claim (premise)
Generated paraphrase (hypothesis)
Rating
Prediction
When Presideat Obama was elocted, the market erashed...
The stock market reacted immediately to Pres- ident Obama's election
Barely
|ENTAIL,
The claim implics a causal relationshp
-true
NEUTRALS
Rhode Island is "almost dead last"... ... in the length of time first-degrce murderers must spend prison before they'r re eligible parole.
Rhode Island is one of the states... where mur- derers must spend the longest time in prison hefore heing eligihle for purole.
ENTTAIL,
"dead last" may mean shortest or longest. de-
True
NEUTRAL,
CONTRADICT
pending stunce
Donald Trump even said, on his very first day in office. he would require every school in Amer- ica let people carry guns our classrooms,
Donald Trump said on his first day in office that every school in America would have to True
"on his first day" may describe either the say- ing or the requiring
ENTAII. NEUTRAL
allow people to carry guns classrooms.
Table 6: Political claims flagged as ambiguous by our detection method. For the claim in the first row, the ambiguity was noted by the fact checker (Rating column), thus leading to a barely-true rating; in the bottom two, the ambiguity was not mentioned, showing the value of this method for ambiguity detection.
there are often issues of ambiguous or under- specified event and entity references (Min et al.. 2020; Cole et al., 2023), leading to work on gen- erating clarification questions (Kuhn et al., 2023; Krasheninnikov et al., 2022). In particular, our ap- proach to ambiguity is inspired by AMBIGQA (Min et al., 2020), where the task input disambiguated in natural language to account for variation in pos- sible outputs. In contrast to open-domain ques- tions, AMBIENT contains more diverse linguistic ambiguities whose resolution is a prerequisite to understanding meaning.
2020; Uma et al., 2021), potentially as a function of their demographic characteristics (Gordon et al.. 2022). In our work, we argue that when uncertainty is in the input, we should instead directly charac- terize the underlying reasons for the uncertainty.
NLI beyond three-way classification For NLI, the seminal work investigating label variation was Pavlick and Kwiatkowski (2019), and subsequent work collected more annotations (Nie et al., 2020) and modeled this variation (Zhou et al., 2022;
Zhang et al., 2021). Other approaches aim to pre- diet the probability of entailment (Chen et al., 2020; Zhang et al., 2017) or fourth "disagreement" la- bel (Zhang and de Marneffe, 2021). We contribute another approach, where NLI models predict the set of labels for plausible readings.
Recent work has also studied ambiguous lan- guage in multi-modal settings: Stengel- et al. (2023) collected set of ambiguous questions about images, and Pezzelle (2023) consider how vision- language models handle underspecified captions.
Jiang and de Marneffe (2022) investigate MNLI data to taxonomize sources of disagreement, and identify "uncertainty in sentence meaning" as source, though they named only lexical and im- plicature ambiguities. Our benchmark includes a wider coverage of ambiguities and our analysis fur- ther sheds light on the nature of the "disagreement."
Other work studies whether the confidence of coreference and II.I models is sensitive to ambigu- input (Yuan al.,
ity in synthetically 2023; Ban et al., 2022). Going beyond task specific models, we evaluate pretrained LMs for the lan- guage skill of managing ambiguity.
Human label variation Human label variation (Plank, 2022) is a broad phenomenon with three dis- tinct sources, summarized by Jiang and de Marn effe (2022): task ambiguity, subjectivity of annota- tor attitudes, and input ambiguity (our focus). Ex- plored contemporary work (Tamkin et al., 2023), task ambiguity arises when the task is underspeci- fied with respect to the desired output; subjectivity is observed when different people disagree, such as for toxic language detection (Sap et al., 2022).
8 Conclusion
Ambiguity in language will become increasingly conspicuous as we push the limits of LM capabili- ties and build tools that engage with the nuances of natural language communication. We develop the first benchmark to evaluate whether language mod- els recognize different readings of ambiguous text, and demonstrate that the task remains extremely challenging. We encourage future work study the sensitivity of LMs context and emphasis, investi- gate the presence of systematic biases in interpreta- tion, and explore the promising space of rorl-world applications enabled by ambiguity- tools.
There is growing recognition of and interest in studying this variation, where the dominant ap- proach is to model the distribution of human judg- ments (Pavlick and Kwiatkowski, 2019; Nie et al.,

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Acknowledgments
Ethics Statement
We would like to thank Nathan Schneider, Ellie Pavlick, Doug Downey, Ewin Tang, Roy Schwartz, Yanai Elazar, Valentina Pyatkin, and Ari Holtzman, well as the greater UW NLP and A12 community, for valuable feedback and discussion at different stages of this work. Our dataset would not have been possible without the expertise of our linguist annotators, which include Emma Miller, Sofia Y. Ahmed, Wendy Kempsell Jacinto, Maxine Appel, Edi Xin, Magdelina Thornton, Huijae Seo, Gita Dhungana, and Aldrich Gran Lapid, and 28 others. This work was funded in part by the DARPA MCS program through NIWC Pacific (N66001-19- 2-4031). We thank OpenAI for offering access to various models through the API. The first author is supported by the National Science Foundation Graduate Research Fellowship Program.
We acknowledge that text generated from language models is susceptible to perpetuating social harms and containing toxic language (Sheng et al., 2019; Gehman et al., 2020). To address this, the anno- tators and validators of our dataset ($2.3) were instructed to discard any examples that may be perceived as offensive. Nonetheless, it is possi- ble that subtly harmful examples may have been overlooked and included in the final dataset.
addition, we are cognizant of the asymmetri- cal relationship between requesters and workers in crowdsourcing ($3). We took great care to pay fair wages, with median hourly rate of $19.13 13,13, and were responsive to feedback and questions through- out the process (see $B.1 for details). The only personal information collect is the worker IDs from Amazon Mechanical Turk, which we will not release. Both the linguist annotation ($2.3) and crowdworker experiment ($3) received IRB exemp- tion.
Limitations
In this work collect broad-coverage dataset of ambiguities, but the size and diversity are nonethe- less limited due the data sources and the effort required for expert annotation. We thus encourage future work to collect more data in the format of AMBIENT, especially for naturally-occuring am- biguities. In addition, we only study ambiguity phe- nomena in English, but how ambiguity manifests in other languages can vary greatly due to systematic typological factors or idiosyncratic differences. For example, while AMBIENT does not contain many instances of morphological ambiguity, these are very common in morphologically richer languages such as Turkish and Finnish. A systematic exten- sion of our dataset and analyses to other languages would be exciting future work.
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A Dataset Creation Details
A.1 Curated Examples
A.3 Linguist annotation
The first author skimmed through several exist- ing NLI datasets and manually identified examples that were both natural and contained salient ambi- guities. Only few examples were chosen from each dataset, avoid overly redundant examples in AMBIENT. In Table 7, we show an example from cach of the sources we drew from. They are directly annotated with the set of labels and disam- biguations by the first author.
Of the generated examples, we ultimately annotate only 2,616 of them. This is due to the slow pace of expert annotation the project went on, and the diminishing returns of annotating more data. Each example was annotated by two linguists. We dis- card an example if either linguist chose to discard it. The final set of examples is 2,020.
Our expert annotators were 37 university-Ievel linguistics students at the University of Washing- ton, recruited through Linguistics mailing list. They were paid $20/hour, in addition to $0.05 per example.
A.2 Generated Examples
The template for prompting GPT-3 to gener- ate unlabeled NLI examples is shown in Ta- ble 8. The model we used is InstructGPT (text-davinci-002), queried on September 4, 2022, with top p- = 0.9 (Holtzman et al., 2020), max tokens 120, and stop sequence "An\n". the generated output is not correctly formatted with
A.4 Validation by authors
The authors review all 2,020 examples, each with two annotations, combining and revising them into a single coherent annotation, or optionally discard- ing the example. The authors validated the ex-

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Example
Disambiguation
Disambiguation
Source
P: is the only pussibility of making the law the servant of the people, not the other way around.
P:.. of making the law the of the people, it should ENTAIL
P: the only possibility that would lead to the law... NEUTRAL
WANLI test
The should people, not the other way around.
servant
[ENTAIL, NEUTRAL
Then he sobered. H: was drunk. [ENTAAL,
P: Then he sobered after drinking alcohol.
P: Then sensible. NEUTRAL
he
became
MNLI dev
ENTAIL
P: Patrick did not manage leave.
P: ... despite his attempt. ENTAIL
P: whether or not he tried. NEUTRAL
H: Patrick tried leave.
IMPPRES
(ENTAIL, NEUTRAL;
P: LaBeouf had tried to bumn smoke from that of them
strangers, was a police officer.
LaBeouf had tried find police officer to bum smoke from. ENTAIL
LeBeouf had tried bum smoke from someone who happened to be police officer.
NLI Diagnostics
H: LaBeouf had tried to bum a smoke
from police officer.
CONTRADICTION
[ENTAIL, CONTRADICTION)
P: Jenny and Zoe solved the puzzle.
P: solved the puzzle together. ENTAIL
... each solved puzzle. CONTRADICTION
H: They solved it together. [ENTAIL, CONTRADICTIONS
DisILL
John opened the door again. H: John opened the door before.
P: John opened the door before, and did it again. ENTAIL
The dour was open before, and John opened the door again. NEUTRAL
P:
Camie
[ENTAIL, NEUTRAL
John wishes to marry Adrienne, Frenchwoman.
P: John wants marry a certain woman who French. ENTAIL
P: John wants for future wife to be French, NEUTRAL
Kearns
H: John wants marry Frenchwoman.
INNTAIL,
P: You should visit Norway in the summer.
P: You should visit Norway the coming summer.
You should visit Norway the summer season.
H: Summer is a good season to visit Nor-
Handwritten
way.
ENTAIL
NEUTRAL
[ENTAIL, NEUTRAL
Table An example AMBIENT from each of the sources we draw from for the curated examples ($2.1).
amples together on Zoom calls over the course of several weeks, actively discussing examples that they were unsure about and developing consistent standards. For instance, we chose to discard exam- ples that boiled down to temporal ordering (e.g.. "I didn't realize that left my keys at home" either entails or contradicts "I realized left my keys at home", depending on the ordering of the sentences) or vagueness (e.g., "He is six feet tall" may or may not entail "He is tall" due to the vagueness of the word "tall"), This process revealed to the extent of task underspecification in NLI (something do not directly study in this work), and allowed us focus on linguistic ambiguity.
A.5 Additional statistics
The disambiguating rewrites are, average, 2.36 words longer than their ambiguous counterparts. Among the ambiguous examples, 74.3% have am- biguity in the premise and 32.6% in the hypothesis, with 6.9% having ambiguity both. 97.5% ambiguous sentences are labeled with two disam- biguating rewrites, with the rest having three or more.
A.6 Ambiguity category annotation
The authors construct taxonomy of ambiguity types by reviewing AMBIENT examples and cate- gorizing possible sources of ambiguity, described in Table 9.
Ultimately, 1,503 examples emerge from this
phase.
Then two of the authors annotate 100 randomly

---

Write pairs of sentences that related each other in the same way.
Cutegory
Description
Lexical
lexical item has different senses
Sentence 1: In the past, have been of the opinion that free market economy is superior economic system.
Syntactic
Different syntactic parses
interpretarions
Figurative
Literal and figurative readings present
Prugmatic
Literal and pragontic interpretations are present
Sentence 2: have changed my mind and now believe that planned economy is superior.
Ambiguity from the relative scopal order of quantifiers OR the scope of particular moditiers
Scopal
Corefereniia
Ambiguous coreference
Sentence 1: would like go to the circus.
Other
Ambiguity that does not fall into the above categories
Sentence 2: have never been to the circus.
Sentence I: For long time, this concept of "collective responsibility" was more important than the need to protect the individual.
Table Ambiguity categories.
Sentence 2: This concept of "collective responsibility" is no longer important.
in the actual project. Through poll taken after the annotation phase was completed, we find that all but one of the participants spoke English as a native language.
Sentence 1: When I was young, I was obsessed with the supernatural.
Sentence 2: am not obsessed with the supernatural any- more.
For the remainder of the study, crowdworkers were paid $0.40 per NLI example, which involved labeling the original ambignous example, assessing the plausibility of three interpretations, and finally labeling three (closely related) NLI examples, At the end of data collection, we aggregate the earning and time spent from each crowdworker, and find that the median hourly rate was $19.13.
Sentence
Table 8: Prompt template for GPT-3 used to create un- labeled examples for annotation, formatted with an ac- tual set in-context examples. In-context examples are from WANLI and found automatically via nearest neighbors in [CLS] token embedding space of an NLI model finetuned on WANLI. All the examples demon- strate a shared ambiguity pattern where sentences about the past or desires about the future induce a cancellable implicature about the present. For instance, When was young, I was obsessed with the supernatural implies that "I" am no longer obsessed, and would like to go the cirews might be taken to imply (more tenuously) that "T" have not been before.
B.2 Setup details
To create a "distractor" sentence among the true dis- ambiguations, we use back-translation with Yorùbá by employing the NLLB model (Meta, 2022) with greedy decoding for both Eng- and Yor-Eng.
In case the generated distractor was an exact copy of the original ambiguous sentence, we repeat the Yor-Eng leg of backtranslation with multino- mial beam search, with a beam size of 5.0, top = 1.0, and temperature / = 2.0. Of the se- quences returned, we randomly choose sequence that is distinct from the original source sentence. For instance, "It is currently March, and they plan to have their wedding scheduled for next De- cember" back-translated to "It is March, and they are to be married in December," which is a faithful though somewhat lossy paraphrase, and 8/9 crowd- workers consider this a possible interpretation. On the other hand, "There will be more interesting sem- inars next quarter" is back-translated to "There will be many more exciting conventions in the next half." which is not a faithful paraphrase and considered possible interpretation by 1/9 workers.
sampled examples from AMBIENT for the ambi- guity type. Each ambiguity is labeled with one category; examples may have multiple categories when they contain multiple ambiguities (e.g.. both premise and hypothesis are ambiguous, or one sen- tence has multiple ambiguous parts). When multi- ple categories are plausible for a single ambiguity (e.g., a word is lexically ambiguous but pragmatics encourages the reading of one over the other), we choose the first one in the order of the table (here, lexical).
Note that the distribution of ambiguity in AMBIENT does not necessarily reflect that of naturally-occurring ambiguity.
B
Crowdworker experiment details
B.1 The crowdworkers
To qualify workers, we designed qualification test with 5 questions that paid $5.00, open the 64 annotators who revised and labeled NLI examples for the creation of WANLI. Of the 43 workers tak- ing the test, 34 passed, though only 29 participated
C LM Experiment details and discussion
C.1
Generating Disambiguations
For the test in $4.1, there is a different template for when the premise ambiguous and when the

---

hypothesis is ambiguuus. For simplicity, we ex- clude the 6.9% of examples where both the premise and hypothesis are ambiguous. The former tem- plate shown Table 2; the latter contains only minor modifications. The instruction is "In each example, you will be given some context and claim. Unfortunately, the claim has some ambi- guity that affects whether it is correct. Enumerate three interpretations of the claim that lead different judgments about its correctness." Then, immediately following the statement of the context and claim, "We don't 't know, because the claim can be interpreted in many different ways:".
2
Avg
FLAN-T5 (xx1)
85.9
28.2
100.0
11.6
56,4
LLaMa (658)
96.1
92.1
11.8
19.9
55.0
GPT-3 (davinci)
46.2
69.0
45.0
71.1
57.8
InstructOPT (-003)
71.9
18.1
81.0
27.5
49.6
ChatGPT GPT-4
81.5
51.7
74.5
23.4
57.7
91.6
68.8
81.8
9.9
63.0
Table 10: Accuracy of LMs on the four templates from the True/False evaluation $4.2. The Avg. column is the one reported in the T/F Acc. column of Table 4.
C.3 Recognizing Interpretation-Speeffi Continuations
EDIT-F1 The EDIT-F1 metric represents a dis- ambiguation by its added and deleted unigrams, and computes the F1 score between the refer- ence and the prediction. For instance, the am- biguous sentence "We't re afraid that LMs aren't 't modeling ambiguity" can be disambiguated with edits 1 -afraid, tworried J. Predicted edits I mmoddeliig +representing S would receive an EDIT-FI of zero, whereas sentence-similarrty metrics like BLEU would give undue credit for the high overlap between preserved portions of the ambiguous sentence.
This section includes implementation details and discussion for the test $4.3.
KL divergence For given disambiguation di let X be random variable equal
P(c d:) P(c a)
Te log
with prob. Pe P(c d,)
In $4.3, we calculate the mean over Xj. indepen- dent and identically distributed copics of X:
N X,,
X
N
Analysis One strategy for attempting disam- biguation we observe across model classes is restat- ing the ambiguous sentence with additional con- text that directly affirms or negates the hypothesis, rather than making a targeted revision to clarify the ambiguity. In some cases, this "shorteut" does lead technically correct disambiguations (and marked such in human evaluation). For instance, for
First we show that is an unbiased estimator for the KI. divergence.
E[X,] E[X]
= Σ Pote
CEx
P(c d;) P(c a)
P(c| d:) log
P: He always ignores his mother's advice to follow his own dreams. H: He follows his dreams.
e D(P(- d.)) P(- a))
where the first step follows from the linearity of expectation.
ChatGPT disambiguates the premise by restating it. followed by "and therefore does follow his dreams" versus "and therefore does not follow his dreams." The former forces the interpretation that he ignores her advice in order follow his dreams; the latter the interpretation that his mother's advice is for him to follow his dreams. Thus, the human-judged correctness may overestimate the models' ability to precisely report the source of ambiguity.
And from the law of large numbers, we observe that X,, tends to the KL divergence the limit.
lim XS [[ D(P(- d)) Il P(- a))
#-000
Prepending a stem to the beginning of the disambiguation (or distrac- tor), for both generating continuations and measur- ing the likelihood of generated continuations. For instruction-tuned models, we append the prompt "Write story. Pe." so that generating on- topic continuations is consistent with its instruction- following objective. For vanilla LMs, we append
We append one of two stems
C.2 Recognizing Disambiguations
For the test $4.2, accuracy on each template is shown Table 10.

---

start quotation mark which find leads to significantly more topical continuations; otherwise, models may generate a newline and proceed to a new topic.
distractor for this in order to make a judgment about the performance of the model.
D Multilabel Model Experiments
Creating the distractor To create the distractor for an ambiguous sentence, we tokenize the sen- tence using spacy and randomly select a word w with the tag NOUN or PROPN (proper noun). Then we find the category node e where w has the IsA relation to e, i.e., w - c. with the largest weight. Finally, we randomly sample a same-category node w * w, representing a single word, such that w
D.1 Methods
Here we describe the setup of NLI models that pre- diet multiple labels as output ($5.1). Multilabel models train separate binary classifier heads for each label on top of the transformer output. Dur- ing inference, the labels are independently selected based threshold (shared across labels) tuned the development set maximize FI. Regression models train a regressor into [0, 1] that represents the probability of hypothesis being true given the premise. The development set is used to select a mapping from each NLI label into a continuous sub-range, and at inference time we pick all la- bels whose ranges overlap with the regressed value. Classifier over sets is seven-way classifier over the power set of NLI labels minus the empty set, As it directly predicts a set of labels, this model requires no threshold tuning.
Sometimes this replacement is not viable, e-g.. when there are no nouns in the sentence, the noun is not in ConceptNet, or there are no same-category words. In this case, we next attempt to replace a pronoun with another heuristically-detenn pronoun; failing all else, we randomly replace any noun or pronoun with the word "corgi."
Generating continuations Given either a true disambiguation or distractor as context, we gener- ate continuations by sampling 100 single-sentence continuations from the full probability distribution, i.e., with top p 1.0. To obtain a single sentence, we stop generation when a sentence-ending punc- tuation mark (one of ?, and is generated, and append a period back.
The median thresholds across 5 seeds from our experiments are shown in Table 11.
D.2 Training Details
For models from prior work, we replicate the train- ing details to the best of our ability. All models are based roberta-large
Limitations Finally discuss some limitations we observed with this test. First, the likelihood of a continuation conditioned on context depends not only on the meaning of the context, but also surface- form attributes like the style and tone, which is a confounding factor in this experiment. Indeed, we observe that there can be stylistic mismatch be- tween original ambiguous sentence and its disam- biguation, often with the latter being more stinted and formal. Generated continuations thus match the formal style, and have lower likelihood under the ambiguous sentence than semantically equiv- alent, more casual paraphrase.
The UNLI model (Chen al., 2020) trained on SNLI's training set (heuristically mapped to regression labels) for 1 epoch, then trained on u- SNLI (human-annotated with regression labels) for 3 epochs.
The AmbiNLI model (Meissner et al., 2021) is first pretrained on single-label data from SNLI MNLI for epochs, then further finetuned on Am- biNLI for 2 epochs. AmbiNLI examples have dis- tributional outputs, and is sourced from the devel- opment set of SNLI and MNLI (which contain 5 labels) and train set of UNLI (which are heuristi- cally mapped soft labels).
In addition, the "closeness" of the distractor af- fects how easy or challenging the test is. We find that in most cases, the noun replacement procedure creates sentence which would expect to have substantially different set of plausible continua- tions, potentially leading the test to be too "casy". Yet this varies with the noun being replaced, the replacement chosen, as well as the overall sentence in which it appears. Nonetheless, we require the
The Distribution Distillation model (Zhou et al., 2022) is trained for epochs on SNLI + MNLI training examples that are re-annotated with the distributional output of teacher model. The teacher model is traditional three-way classifi- cation model trained on SNLI + MNLI.
Finally, the multilabel model from Jiang and de Marneffe (2022) trained on the development

---

Model
Thresholds
(0.69, 1.0)
Reg.
Uncertain NLI (C+20)
N: (0.01, 1.0)
C: (0.03, 0.71)
Multi. Class. Dist.
AmbiNLI (M+21)
-3.43
Dist. Distillation (Z+22)
-1.55
MNLI (M+18)
-2.68
WANLI (L+22)
-1.19
Multi-label MNLI (M+18)
-2.78
Multi-label WANLI
-1.97
Set classifier on WANLI
N/A
Table 11: Logit thresholds used to map the output of var- ious models to a set of labels, for multilabel prediction experiments ($5). The way these thresholds obtained and used at inference- -ieenceeeemmeeee is explained in $D.1.
set of MNLI and ChaosNLI, where a label is con- sidered present if 20% of annotators choose the label. The model with the lowest loss on held-out data over 30 epochs is selected as the final model.
E Political Claims Case Study
To paraphrase each political claim, InstructGPT (text-davinci-003) zero-shot with the simple prompt "Paraphrase the text. مe {Claim} Paraphrase:" and decode with top = 0.9, to encourage both correctness and diversity among generated paraphrases.


Reviews:
[{"review_id": "hfHqYfEJv0", "reviewer": "Reviewer_4NEm", "summary": "", "questions": "A. Did you try alternate prompting strategies when evaluating the pretrained models (e.g. chain-of-thought or self-critique)? If so, what were they?\n\n\nB. Why do the evaluation setups for models and crowdworkers differ so significantly? Perhaps I am misunderstanding the human evaluations… I would really appreciate some conceptual clarification/reasoning about the setup here!", "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 your insightful questions, and we are grateful that you recognize the “strong dataset contribution,”  “value in pursuing research in this domain,” and “headroom for interesting future work”! We hope the following new experiments and discussion address your concerns.\n\n**Exploration of other prompting methods for disambiguation generation (§4.1)**\n\nNote that the prompt in §4.1 is a bit complex because the specified task requires both disambiguation and entailment judgments. This was necessary for our setup to be valid, because a single sentence considered in isolation may have multiple dimensions of ambiguity, while with the full example there is only one that affects the entailment relation. Adding chain-of-thought to this prompt would only make it more complicated, which was your original concern. However, we tried self-critique by asking GPT-4 to critique its last generation, and then revise it based on its critique. We found that **this actually reduced the F1 score from 18.0 to 10.1,** as the style tends to change dramatically in the revision. For instance, the disambiguation “*I saw a crane (the bird) on my walk. Then the claim is true.*” became “*If the term \"crane\" is interpreted as a type of bird, then the claim \"I saw a bird on my walk\" is true.*”\n\nNonetheless, following the spirit of your question about whether GPT-4 can generate disambiguations when given simple and helpful prompts, we ran new experiments **focusing on the simpler task of disambiguation generation alone**. Note that this is *not comparable* to the original task, which requires the predicted NLI label to be correct, but we hope this gives you a sense of how challenging disambiguation is on its own for GPT-4.\n\nWe wrote a new prompt that uses “confidence-boosting” (“*You are an English professor*”) and asks for step-by-step reasoning. The prompt is below (in-context examples are from the dev set):\n\n> You are an English professor. For each ambiguous sentence, analyze the sentence step by step based on your knowledge of the English language, then identify a set of distinct interpretations.  \n> 1. **Ambiguous sentence:** Alice and Jon disagreed.  \n**Reasoning:** It is possible that Alice and Jon disagreed with each other, or that they both disagreed with something else.  \n**Interpretations:**  \na) Alice and Jon disagreed with something.  \nb) Alice and Jon disagreed with each other.  \n2. **Ambiguous sentence:** I asked the participant if they were a US citizen or a Green Card holder.  \n**Reasoning:** It is possible that the speaker thinks the participant is either a US citizen or a Green Card holder, and wants to know which of the two they are. It is also possible that the speaker wants to know whether the participant is either a citizen or Green Card holder, as opposed to neither.  \n**Interpretations:**  \na) I asked the participant if they were a US citizen or if they were a Green Card holder.  \nb) I asked the participant if they were either a US citizen or a Green Card holder, or neither.  \n3. **Ambiguous sentence:** It takes a long time to find a room in this building.  \n**Reasoning:** This may mean that it takes a long time to find an available room due to the building being crowded, or that the building is hard to navigate and it takes a long time to find a given room.  \n**Interpretations:**  \na) It takes a long time to find an available room in this building.  \nb) It takes a long time to locate a room in this building.  \n4. **Ambiguous sentence:** Some of the furniture in the store is on sale.  \n**Reasoning:** This sentence has a cancellable implicature that some, but not all, of the furniture is on sale.  \n**Interpretations:**  \na) Some, but not all, of the furniture in the store is on sale.  \nb) Some, and possibly all, of the furniture in the store is on sale.  \n5. **Ambiguous sentence:** {ambiguous_sentence}  \n**Reasoning:**  \n\nDue to the possibility of many valid disambiguations in this setting, we do human evaluation of 50 examples (we mark generated disambiguations as correct as long as it depicts ANY valid ambiguity; often they are different from the reference). The correctness is 48%, showing that GPT-4 can meaningfully disambiguate in many cases, but there is still huge room for improvement; additionally, it is still important for GPT-4 to disambiguate in context (the original setting).\n \nIf you have a particular prompting idea that we have not explored, we are happy to experiment with that!\n\n**Comparability of human and model performance**\n\nThank you for the very thoughtful question about the comparability of human vs. model performance. We will improve the clarity of this point in the paper based on your feedback. \n\nTo be clear, we never claim \"human performance in generating correct disambiguations,\" as you are right that we do not test crowdworkers' ability to directly generate disambiguations. The 89.7% statistic is the correctness of AmbiEnt as judged by crowdworkers. This setup is very different from what we subject pretrained LMs to, so we do not actually refer to it as “human performance” anywhere in §4.\n\nTo summarize, we use the 89.7% statistic as a comparison point in two places:\n\n1. In §4.1, we say that the human evaluation of LM-generated disambiguations \"can be directly compared to the crowdworker-judged correctness of AmbiEnt itself at 89.7%\" (L. 349 -351). This is true because model-generated disambiguations and AmbiEnt disambiguations are subject to the same evaluation. Here, we are not purporting to make any claim about human performance on the task. Instead, crowdworkers are a fixed evaluation method, and we are providing a way to contextualize the performance of LMs. (That is, 89.7% is a kind of upper bound.)\n\n2. In §5, where we evaluate multi-label NLI models, we do recontextualize 89.7% as human performance. The idea is that if crowdworkers can recognize disambiguations and identify the corresponding label for each disambiguation, then they would be able to choose the correct set of labels, which is the same task given to multi-label NLI models.  \nTo be more clear here, we can instead describe 89.7% as a “crowdworker oracle performance,\" where crowdworkers effectively predict the set of labels by being presented with possible disambiguations.\n\nIn any case, to reduce confusion, we will not introduce the 89.7% statistic in §3.2 as “human performance,” and instead describe it as “crowdworker-judged correctness” of the dataset. Please let us know if this would address your concerns.\n\n**Are there ways to obtain sequence probabilities from GPT-3.5/4 for §4.3?**\n\nIf the GPT-4 API allowed access to its probabilities, we agree these results would be illuminating for §4.3.\n\nIn theory, it is possible to estimate the probability of sequences using Monte Carlo Sampling. However, this is only tractable when the output space is small (e.g., when the probability mass is concentrated on “*Yes*” or “*No*”). With longer sequences, the output space grows exponentially, and one needs intractably many samples to obtain an estimate with low expected error — making it infeasible both in terms of time and API cost.\n\n**Discussion of prior work about overcoming ambiguity**\n\nThank you for sharing the interesting related work, we will discuss these papers in the next revision. In this work we are more interested in whether LMs possess an ability, rather than whether they can learn it through finetuning (which is a distinct research question). Nonetheless, we did finetune RoBERTa on existing NLI datasets with label variation in §5, in order to connect with the extensive work on predicting disagreement in NLI (which all tune RoBERTa); the best EM accuracy here is 43.6% (Table 5). We acknowledge that finetuning is a promising method for creating dedicated disambiguation models.", "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": "* Well-motivated: the authors clearly demonstrate that models have significant room for improvement in ambiguous instances. They also show the value in pursuing research in this domain through demonstrating the presence of real-world ambiguous constructions in political spheres. \n* Strong dataset contribution: the authors provide a robust NLI dataset of ambiguous entailments that can be used in future work to measure model’s performance with underspecified information.\n* Overall, a really compelling pipeline for studying ambiguity further and leaves a lot of headroom for interesting future work!", "reasons_to_reject": "Weaknesses\n* Lacks a good faith effort to improve model’s performance via prompting: although I understand the need for LLMs to disambiguate these ambiguous inputs, I am unconvinced that with robust prompting, that models would be unable to do this. Judging from the prompts provided in Table 2, the authors fail to utilize modern prompting techniques (chain-of-thought, self critique) which demonstrably improve model performance in a variety of contexts. Furthermore, I personally found the prompts difficult to parse as well. I think a more variable prompting setup is necessary to explore. I couldn’t find any information regarding alternate prompts the authors considered/tested prior to settling on the prompting framework in Table 2. \n* Mismatched evaluations between human performance and model performance: the authors argue that even the best model (GPT-4) does not match human performance in generating correct disambiguations. The authors utilize crowdworkers to identify if a given disambiguating construction (either model or directly from the dataset) is viable given the premise for the construction. However:\n* 1) the authors do not actually test human performance on this task, instead arguing that the crowdworker’s classification of the model’s generations can be directly compared to their judgment of correctness for AmbiEnt itself. I find this logic to be unconvincing as the crowdworkers themselves were not asked to generate disambiguating evidence – AmbiEnt was carefully curated by linguists to purposefully have viable disambiguating evidence. I would imagine crowdsourced disambiguating generations are not as convincing as those in AmbiEnt.\n* 2) in line with (1) the methodology for prompting crowdworkers for baseline measurements in Sections 4.1 and 4.2 greatly diverge from the prompts received by the models. For example, when asked to identify disambiguations of a given ambiguous constructions, the crowdworkers received 2 disambiguating constructions and 1 distractor construction. In contrast, models undertook a more complex labeling task in which they were asked to classify a disambiguating example across 4 different interpretations (Table 3). \n\nNice-to-haves\n* When testing LMs’ ability to model context-specific continuations, the authors exclude ChatGPT and GPT-4 because their API does not enable likelihood calculations. Although I understand this limitation, seeing as these two models performed the best on the previous evaluations, estimating likelihood over repeated trials or some other form of likelihood estimation would have been nice to pursue. \n* Prior work has demonstrated the potential of finetuning decoder models in ambiguous contexts. I would be interested to see how a decoder model finetuned on a subset of AmbiEnt performs on the proposed elevations. E.g. Task Ambiguity in Humans and Language Models: https://arxiv.org/abs/2212.10711", "questions_to_authors": "A. Did you try alternate prompting strategies when evaluating the pretrained models (e.g. chain-of-thought or self-critique)? If so, what were they?\n\n\nB. Why do the evaluation setups for models and crowdworkers differ so significantly? Perhaps I am misunderstanding the human evaluations… I would really appreciate some conceptual clarification/reasoning about the setup here!", "paper_topic_and_main_contributions": "In this paper, the authors propose a novel NLI benchmark dataset of 1,645 ambiguous entailment relations (AmbiEnt). They explore LLMs’ (Flan-T5, LLaMa, GPT-3, ChatGPT, InstructGPT, GPT-4) disambiguation of the entailment relations, finding that LLMs struggle to 1) identify ambiguous constructions and 2) provide disambiguating evidence. Calculating the KL divergence between possible disambiguations for a given construction and unrelated distractor constructions, the authors show that models have room for growth in identifying the correct continuation of ambiguous sentences. The authors finetune NLI models on existing datasets with label variation, finding that these models also struggle to predict the label set for the examples in AmbiEnt. Lastly, they well-motivate their work on ambiguity through demonstrating the use of a multilabel NLI model to detect misleading political claims “in the wild.”", "excitement": 4, "reproducibility": 5, "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": {"Missing_References": "In the RW, I would have liked to see a discussion of efforts to overcome this and how these fall short of the task you have proposed here…\n\n\nEfforts to overcome ambiguity:\n* CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models: https://arxiv.org/abs/2212.07769\n* Assistance with large language models: https://openreview.net/pdf?id=OE9V81spp6B\n\n\nPromise for finetuning decoders as a way to overcome ambiguity: \n* Task Ambiguity in Humans and Language Models: https://arxiv.org/abs/2212.10711"}}, {"review_id": "MxvjpH5xgd", "reviewer": "Reviewer_Eef3", "summary": "", "questions": "* With the AmbiEnt dataset, the (premise, hypothesis) pairs with single NLI labels are considered unambiguous. Wouldn't it be possible to have some ambiguity with the premise or the hypothesis whose interpretations do not affect the NLP label?\n* Even though language is ambiguous and we formulate ambiguous communications, not all interpretations of an ambiguous text are created equal. We have the Grice's cooperative principle and, for most of the examples included in the article/appendix, one of the disambiguations seems more likely than the other, which sometimes feels like a stretch. For instance, why say \"Patrick did not manage to leave\" to mean \"whether or not he tried\" (disambiguation 2)? \"Patrick did not leave\" would be more straightforward and adding the extra \"manage to\" implies an attempt (disambiguation 1).\n* Any insights on how some of the values used for probability thresholds and other settings were selected?\n* Isn't the distractor used for candidate interpretations of an ambiguous sentence too different from the actual disambiguations given that it is automatically generated by translation back and forth in a low-resource language when it comes to form? Are these distractors grammatically correct? Do they have correct capitalization?", "limitations": "", "rating": 4, "confidence": 3, "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 recognizing the value of our dataset and the insightful questions. We hope the following clarifications address your concerns.\n\n**What about ambiguities that don’t affect the NLI label?**\n\nIndeed, we only consider ambiguities in NLI examples that affect the determination of the label. This is by design. Natural language is rife with ambiguities — consider this very innocuous sentence: “*John and I are going to the bank.*” Does this mean John and I are going together, or possibly separately? Are we presently in the process of going, or do we have plans to go in the future? Are we going to the river bank, or the financial institution? [English Resource Grammar](https://delph-in.github.io/delphin-viz/demo/) actually lists 22 different semantic representations of this sentence. It is really hard to study ambiguities at such a fine-grained level (see our footnote 2), and so we purposefully capture ambiguities that affect NLI labels, to provide a natural setting where ambiguities are contextually relevant. We will make this design choice more clear in the next revision!\n\nNote that the presence of other ambiguities does not affect the validity of our experiments. For  §4.1, we provide both the premise and hypothesis, and ask about relevant ambiguities; in §4.1, all of the True/False constructions are valid even if there are more interpretations than we consider, and likewise for the ranking test in §4.3. For §5, of course we provide the full NLI example for multi-label prediction.\n\n**Not all interpretations are created equal**\n\nWe agree that for many examples, there is a more likely interpretation! This is sometimes exactly what makes this problem worth studying: as we emphasize in the political claims case study, speakers can take advantage of this to mislead their listeners into hearing something more than they're actually able to defend. For instance, \"*Since I took office, Wisconsin now has the 2nd highest health care ranking in the country*\" is a clear violation of the Gricean relevance principle, since in reality, Wisconsin was already 1st or 2nd before he took office! [[cite](https://www.politifact.com/factchecks/2015/aug/05/scott-walker/scott-walker-says-he-took-office-wisconsin-has-ran/)] Yet, that is precisely the speaker's intent, to mislead his listeners without necessarily lying. In these cases, it is important to pick out the less likely interpretation!\n\nFor the particular example you pointed out, imagine someone saying \"*I did not manage to fix the problem*\" to imply they made an effort, when in reality maybe they didn’t.\n\nBesides this, there are many cases where speakers are not necessarily considering the range of possible interpretations from the listener’s perspective, OR “relative likelihoods” between different communicators do not align — and miscommunication occurs.\n\n**Probability thresholds are selected using the AmbiEnt dev set (Appendix D.1)**\n\nThis information is currently in Appendix D.1, but we will move it to the main paper for the final version. In short, for each model, we use the AmbiEnt dev set to select threshold(s) that would produce the best F1 score. For instance, the regression NLI model, for each label, we search the space of $[0, 1] \\times [0, 1]$ for a pair of endpoints. Then at inference time, we predict all labels whose ranges overlap with the regressed value. The actual thresholds are in Table 11.\n\n**The back-translated distractor has correct form**\n\nAll distractors have correct grammar and punctuation, and additionally we were careful to manually ensure they are stylistically similar to the disambiguations. In fact, many of the distractors are faithful paraphrases, and crowdworkers indicate that they are plausible interpretations 46.7% of the time (L. 288), meaning that they are reasonable paraphrases.\n\nHere are some examples of back-translations.\n\n- “The novel has not been banned in many schools.” $\\rightarrow$ “Many schools have not banned the book.”\n- “It is difficult to believe that the author of such a masterpiece could have been only twenty-three years old when he wrote it.” $\\rightarrow$ “It is hard to believe that the author of this remarkable work was only 33 years old when he wrote it.”\n\nThe first is a faithful paraphrase, and the second one should say “23” instead of “33” but is otherwise correct.", "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": "* AmbiEnt (Ambiguity in Entailment) -- collection of 1,645 examples with lexical, syntactic, and pragmatic ambiguities in 35% of examples\n* Manual (expert and naive) and automatic efforts are used to create the dataset with several checks in place, including agreement computations\n* Several ambiguity-related experiments are conducted, whose findings are outlined in the paper\n* Code and data to be made publicly available", "reasons_to_reject": "* Some issues related to the dataset-generation process. Please see questions below.", "questions_to_authors": "* With the AmbiEnt dataset, the (premise, hypothesis) pairs with single NLI labels are considered unambiguous. Wouldn't it be possible to have some ambiguity with the premise or the hypothesis whose interpretations do not affect the NLP label?\n* Even though language is ambiguous and we formulate ambiguous communications, not all interpretations of an ambiguous text are created equal. We have the Grice's cooperative principle and, for most of the examples included in the article/appendix, one of the disambiguations seems more likely than the other, which sometimes feels like a stretch. For instance, why say \"Patrick did not manage to leave\" to mean \"whether or not he tried\" (disambiguation 2)? \"Patrick did not leave\" would be more straightforward and adding the extra \"manage to\" implies an attempt (disambiguation 1).\n* Any insights on how some of the values used for probability thresholds and other settings were selected?\n* Isn't the distractor used for candidate interpretations of an ambiguous sentence too different from the actual disambiguations given that it is automatically generated by translation back and forth in a low-resource language when it comes to form? Are these distractors grammatically correct? Do they have correct capitalization?", "paper_topic_and_main_contributions": "This this article, the authors focus on capturing ambiguity, by (1) creating a dataset of (premise, hypothesis) pairs with ambiguous language (including NLI labels and unambiguous rewrites), (2) evaluating 6 pretrained language models on proposing possible relevant disambiguations, and recognizing disambiguations with GPT-4 achieving the best results, and (3) evaluating roberta-large-based NLI models that predict one single probability value, a distribution over labels, or a multilabel. \n", "excitement": 4, "reproducibility": 5, "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": {}}, {"review_id": "Dggh6zcWqd", "reviewer": "Reviewer_CZrE", "summary": "", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": 5, "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 recognizing the “vital issue” of ambiguity in NLP research, the value of our corpus for “assessment of LLMs… or even for linguists,” and our “sound” experiments!\n\n**Analysis by category**\n\nWe only manually labeled 100 AmbiEnt examples with category annotations, and thus we are wary of drawing quantitative conclusions. However, we have qualitatively observed that models are quite strong at dealing with syntactic and coreference ambiguities, but much weaker at pragmatic and scopal ambiguities.\n\nFor possible future work, we are considering expanding AmbiEnt along with category annotations, in order to do category-based analysis and study systematic preferences in interpretation!\n\n**Which types of ambiguities are covered, and which are not?**\n\nWe hope that our category annotation (shown in Table 1) partially addresses this, by breaking down the types of ambiguity (pragmatic, lexical, syntactic, scopal, coreferential, figurative, other) present in our dataset. \n\nIndeed, there are many types of ambiguity that are rare or missing from our dataset because they do not appear much in our data sources (e.g., slang), or they are hard to capture with our set-up (e.g., sarcasm, which would require more conversational context), or they are rare in English (e.g., morphological ambiguities, briefly discussed in Limitations). We will include a more thorough discussion of this in the Limitations section!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper investigates a very interesting and vital issue in language, ambiguity.\n2. The corpus is not only useful for the assessment of LLMs but also for the general NLI community or even for linguists.\n3. The construction process of the corpus is well-designed making the resulting corpus very much reliable.\n4. The assessment experiments are inclusive and the conclusions are backed by sound pieces of evidence.", "reasons_to_reject": "I don't see major issues in this paper. Here are two minor issues.\n\nThe results would be more interesting if they can be grouped with respect to (1) how the data was collected and (2) the type of ambiguity (lex, syn or pragmatic). \n\nIt is also vital to discuss what types of ambiguity have been covered and what types have not. This help readers to understand the generalizability of the findings.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The current paper investigates ambiguity. To this end, the authors:\n1. built an NLI corpus with lexical, syntactic and pragmatic ambiguity, namely, AmbiEnt.\n2. conducted a crowdsourcing experiment to confirm that ambiguity is the major source of disagreements among annotators for NLI.\n3. based on AmbiEnt, assessed ChatGPT and GPT-4 and concluded that ambiguity is still challenging for them even though they are fine-tuned on underspecification dn subjectivity NLI corpora. ", "excitement": 5, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Maybe some classic paper about the theory of ambiguity in computational linguistics? For example:\n\nSemantic ambiguity and perceived ambiguity, by Massimo Poesio"}, "extra_text": {}}, {"review_id": "rHQz2jK5VA", "reviewer": "Reviewer_LLmw", "summary": "", "questions": "a) With growing number of available datasets for NLI, how do the authors foresee extending and maintaining this dataset? ", "limitations": "", "rating": 4, "confidence": 2, "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 recognizing that ambiguity is “an important consideration for LMs” and our “careful collat[ion]” of AmbiEnt. We hope the following clarifications address your review!\n\n**Re: the Bender rule — see Limitations**\n\nWe currently note in the limitations section that we only study English (L. 595). We will state this explicitly in the main text in the next revision.\n\n**On Extending AmbiEnt**\n\nOur main interest was in studying ambiguity through the format of multi-label NLI, as opposed to overhauling the NLI task itself. So it is not crucial to update all NLI datasets to account for ambiguity; all datasets inevitably contain underspecified/ambiguous examples, but they still have merit.\n\nSince this dataset is important for our own research, we expect active maintenance for some time, and have ideas about extending it, e.g., by finding naturally occurring instances of misunderstanding online or constructing examples at scale from Wikipedia disambiguation pages.", "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": "As the authors point out, ambiguity is an important consideration in communication, and by extension for LMs. This paper is well written and presented. The dataset presented was carefully collated, with details of various phenomena distributions described. The motivation of this study is further exemplified by the case study on political claims. ", "reasons_to_reject": "An easily rectifiable point: \nIt would be great if the authors explicitly note the language considered (I assume it's English).  \"Always name the language you are working on\" (Bender 2011: 18)\n\nBender, E. M. (2011). On Achieving and Evaluating Language-Independence in NLP. Linguistic Issues in Language Technology, 6. https://doi.org/10.33011/lilt.v6i.1239", "questions_to_authors": "a) With growing number of available datasets for NLI, how do the authors foresee extending and maintaining this dataset? ", "paper_topic_and_main_contributions": "This paper presents a new dataset AMBIENT. Which is a linguist annotated benchmark of 1,645 examples with diverse kinds of ambiguity. The authors make a strong case on why ambiguity matters. A series of tests are presented for pretrained LMs in generating, recognising and disentangling ambiguity. They present a case study on flag political claims where focusing on disambiguation is beneficial.  ", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "line260-262 full stop missing or misplaced"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper presents a new NLI benchmark and experiments using LLMs. The authors study several ambiguities and demonstrate that crowdworkers disagree less after they consider their disambiguations.

All the reviewers are positive.

The dataset is small (~1,500), and that is fine. Yet the authors only calculated agreement with 50 samples (Section 2.4). I am  surprised they did not do it with all the samples. It is also unclear if the agreement were calculated before or after discarding. The resource is the main contribution of the paper (and the idea / problem). I don't think the prompting experiments add a lot of value other than to say "off-the-shelf LLMs cannot solve this problem" (which is expected).
Decision: Accept-Main