Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual
Conditional Generation with Interaction
Jonathan Pilault 1 2 3 * Xavier Garcia 1 Arthur Bražinskas 4 Orhan Firat 1

Abstract
Crosslingual conditional generation (e.g., machine translation) has long enjoyed the benefitsof scaling. Nonetheless, there are still issues thatscale alone may not overcome. For instance, in
the absence of additional context, a source queryin one language may yield several translation options in another language. Only one translation
could be acceptable however, depending on thetranslator’s preferences and goals. Choosing theincorrect option might significantly affect translation usefulness and quality. We propose a novel
method interactive-chain prompting — a seriesof question, answering and generation intermediate steps between a Translator model and a Usermodel — that reduces translations into a list of
subproblems addressing ambiguities and then resolving such subproblems before producing thefinal translated text. To check ambiguity resolution capabilities and evaluate translation quality,
we create a dataset exhibiting different linguistic
phenomena which lead to ambiguities at inference
for four languages. To encourage further exploration in this direction, we release all datasets.
We note that interactive-chain prompting, usingeight interactions as exemplars, consistently surpasses prompt-based methods with direct accessto background information to resolve ambiguities.

Figure 1. Interactive-Chain-Prompting (I NTER CP T).

size of the pretraining dataset, and the amount of computing
budget gives Language Models better sample efficiency andability to generalize for many tasks (Kaplan et al., 2020;
Brown et al., 2020; Henighan et al., 2020; Hernandez et al.,2021; Lepikhin et al., 2021; Wei et al., 2022a). However, fortasks such as commonsense and symbolic reasoning, where
the solution requires multistep computation, or crosslingual
conditional generation such as Neural Machine Translation(NMT), where there could be more than one plausible prediction for a given source sequence, scale alone may not
be sufficient to achieve high accuracy (Rae et al., 2021;
Ghorbani et al., 2022).
Chain-of-thought (Wei et al., 2022b) and least-to-most(Zhou et al., 2022) methods have demonstrated, by prompting a (large-)LM such as PaLM (Chowdhery et al., 2022),
that breaking down a task into subproblems that are solved
sequentially greatly improves the quality of the final prediction. Such methods demonstrate that producing intermediate sub-results that address specific aspects of a bigger
problem significantly improves performance on tasks likearithmetic, math word problems, and symbolic manipulation.While studies have investigated the translation capabilities of PaLM with various prompting strategies (Vilar et al.,2022; Zhang et al., 2023), prompting large and general purpose LMs such as PaLM to identify and solve subproblems
in crosslingual conditional generation tasks such as NMT
has not yet been fully explored.

1. Introduction
Transformer Language Models (LM, Vaswani et al. 2017)pretrained on large corpora have achieved outstanding results in a variety of NLP benchmarks (Devlin et al., 2019;
Brown et al., 2020). Scaling the number of parameters, the* Work done while at Google 1 Google Research, Brain Team
Mila 3 Polytechnique Montreal 4 Google Research, XGen Team.
Correspondence to: Jonathan Pilault <pilaultj@mila.quebec>,
Orhan Firat <orhanf@google.com>.
2

Interactive Learning with Implicit Human Feedback Workshop
at International Conference on Machine Learning (ICML) 2023,
Honolulu, Hawaii, USA. Copyright 2023 by the author(s).

1Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

Our approach, Interactive-Chain-Prompting (I NTER CP T),
sequentially solves translation subproblems before generating a final translation prediction. As shown in Figure 1,
we first detect ambiguities in translation queries, then weresolve these ambiguities via question-answer interactions,and finally we generate translations. I NTER CP T departs
from other prompt-based techniques that sequentially solve
subproblems in two fundamental ways: (1) the subproblems are related but considerably different to the main taskand (2) the solutions to subproblems requires interaction
with another LLM. In this paper, we will look at how intermediate computation steps and interaction might assist
overcome a typical problem in automated systems when auser’s ambiguous query leads to a large number of viableand potentially inaccurate answers. In translation, for example, selecting the incorrect prediction has a significant
impact on translation quality as illustrated in Fig. 2.

Figure 2. Translation queries with multiple possible predictions.
Correctly solving subproblems around ambiguities with you and it
greatly affects the BLEU (Papineni et al., 2002) translation metric.

I NTER CP T has several advantages. First, the LM is able
to identify and ask questions about translation query ambiguities with only a few in-context exemplars and no finetuning. This is crucial since large corpora with specifictarget ambiguities, labels to classify each ambiguity subtypes (i.e. feminine/masculine for gender or formal/informal
for formality) and context are not common and are typically
low-resource. Then, without readily available context, werely on the User to disambiguate translation queries. In theabsence of additional background information or context,
there are limited options to solve ambiguities. Interaction
with the User stands as a logical way to collect clarifying
information. This interaction also benefits from multiple
computation steps where ambiguity resolution leads to amore precise final prediction. Finally, the question-answertranslation interaction improves transparency and makes it
easier to debug translation systems since we can assess thereasoning chain that led to an error (Wu et al., 2022a). For
NMT, there are two main questions to consider to make themost of out of intermediate computation steps:

we apply least-to-most prompting to math word problems
for example, the answers to subquestions can often be derived from the problem’s text. It is not necessarily the casefor NMT where the query may not contain enough context
to resolve ambiguities. As seen in Fig. 2, English sentence‘S’ does not contain enough information about “you” and“it”. The incorrect prediction made by a model leads tolarge variations in translation quality scores. With more
context, the model may have the necessary information to
narrow down possible predictions. However, in industrial
applications, translation queries are often too short (Badeka,2016) or additional context is not existent. In this work,
we automate interaction between a PaLM Translator model,that detects ambiguities, asks clarifying questions and translates, and a PaLM User model, that has access to context
and answers questions. Both models engage in a multiturn
dialog to zero-in on a narrower set of predictions. We argue
that a type of question-answer interaction with a “user” isnecessary to resolve ambiguous queries, especially when auser (1) is unfamiliar with the main task and may not possess the in-domain knowledge to choose from many model
prediction options; (2) knows how to answer simple pointed
questions about a query but may not be able or willing todecide and add appropriate context on the fly.

A) What subproblem are we trying to solve? Multistep
reasoning tasks can often be explicitly decomposed into subproblems: ambiguity detection, disambiguation via Q&A
and translation. For NMT, decomposing the translation task
is not trivial. We assume in this work that our subproblems
are ambiguities which arise when translating. As seen in
Fig. 1, the first step in I NTER CP T is to discover and resolve
the translation ambiguity subproblem. We study five typesof ambiguities: polysemous words, pronoun resolution, formality, gender-neutral names and neutral professions. Sincedatasets that cover multiple translation ambiguities and language pairs while providing context are rare, we create ourown datasets (see Table 5 in Section C for an overview ofother publicly available datasets).

This work marks Large-LM’s potential to leverage a few
in-context examples, to provide natural language answers
and deliver results closer to a user’s intent.

2. Interactive-Chain-Prompting (I NTER CP T)
When interacting with a model, a user may have somewell-conceived query in mind that is inadvertently underspecified. For example, a monolingual English speaker may
be unaware that the pronoun “you” in a sentence can leadto formal or informal constructs in other languages. The

B) Where do answers to subquestions come from? When2

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

model may therefore not receive additional information on
the level of formality needed to adequately translate the text
by this particular user.

Table 1. A MBIG MT data examples for each ambiguity for target
language x. ∆ B is the BLEU performance drop from 100 if thehighlighted ambiguity is resolved incorrectly.

A human translator, when asked to translate queries with“you”, may want to first probe the user’s latent context about
the query by asking clarifying questions. In doing so, thehuman translator can use the answers to better align thetranslation to a User’s request and context. Our method
endows language models (LMs) with the ability to generate
a similar chain of interactions between a Translator LM
and a User LM as seen in Fig. 1. In real applications, it isexpected that a human replaces the User LM. I NTER CP T
uses in-context exemplars to resolve ambiguities before
completing the crosslingual conditional generation task thatthe model is originally asked to do.

Dataseten QueryContext

x Target∆B“it” reso- He has read it - I remember when the Me la sé de -44
lutionto me so many postcard came, Ernesto memoriadetimes that I’ve was so pleased. - He said: tanto leerla.
learnt it by ”Look what my Rosetta
heart.
has written to me”.
Polysemy head
If you don’t feel well, 先-100head home.
Formality The closer you - I’m aware of the risks, Plus vous serez -58
can get to him, Master Jedi, but I know proche de lui,
the better.
you can regain Clovis’ mieux cela sera.
trust.
Gender Blairshould - I have her doorman on Blair sollte ihr -40
neutralbe wrapping up retainer. - There’s a fine Frühstück
mit
names
[pr] breakfast line between surveillance Beatrice haben.
with Beatrice.
and stalking.
Neutral [pr]worked Margaret
Mhango Previamente, tra- -70
profespreviously as a Mwanakatwe is a Zam- bajó como emsions
businesswoman, bian politician [...]. She presaria, contaaccountant, and was the director for dora y ejecutivabank executive. business development [...] bancaria.

It consists of a three step reasoning chain (See Fig. 1) withdemonstrations that remain constant for each input query:
1. The first step is for identifying ambiguities. The
prompt in this step contains the exemplars, showing multiple queries to translate and questions about each query’s
ambiguities. During inference, the Translator LM uses
the prompt to generate a pointed question that identifies
the specific ambiguity.
2. The second step is for resolving ambiguities. The
prompt in this step contains exemplars answering thequestion to the ambiguity subproblems in step one. The
User LM answers each question using additional information from the provided context. In real life applications,
we assume that a real user has similar background information about the text to be translated.
3. The third step is for translating. Generated questions
and answers are appended to the prompt in step 1 before
the final translation is produced. Constant prompts in thisstep demonstrate how to translate in the specified target
language using only details provided by the User LM
and no-context. During inference, the Translator LM
uses the prompt to generate the translation.

& Tiedemann, 2016). In Section C of the Appendix, we
provide more details on datasets and describe the heuristics
to identify ambiguities in each language.
“it” resolution data contains English sentences where thepronoun “it” does not clearly refer to a noun within thequery. In English, the pronoun “it” is a singular, neuter andimpersonal pronoun. In other languages, “it” may translate
into gender specific pronouns (either feminine or masculine) or get dropped entirely from the sentence. The choicedepends on what the pronoun refers to. To correctly translate, the model must first determine what “it” is. In the firstexample of Table 1 where the target language x is Spanish, knowing that “it” is a postcard, or una tarjeta postal in
Spanish, disambiguates gender in the translation. While thegender affects two words in the target sentence, the wrong
gender choice is not only qualitatively inappropriate but also
decreases quality metrics (44 BLEU score drop from 100).
Polysemy is a dataset that contains words that have multiple meanings and the query is insufficiently informative tozero-in on a specific sense. The context uses the word withina sentence to provide the necessary background information.
In the second example of Table 1 where the target language
x is Japanese, the context shows that “head” is a verb. In
conjunction with the noun “home”, we disambiguate “head”
as “to move in the direction of”. In the absence of suchcontext, “head” has various senses such as “upper part ofthe body”, “side of a coin”, “end of a hammer or tool”, “atoilet on a boat”, “to hit the ball with the head”, “to lead”.

3. Ambiguity MT Datasets (A MBIG MT)
In this section, we introduce A MBIG MT, a dataset that covers four language pairs, for translations from English into
French (en-fr), German (en-de), Spanish (en-es) or Japanese(en-ja) — 18 sub-tasks in total. The parallel translation
corpora contain five types of ambiguities: “it” resolution,formality, polysemy, gender1 neutral names, neutral professions. Unless otherwise specified, all datasets include
1000 diverse samples for each {en-fr, en-de, en-es, en-ja}
language pair extracted from Opensubtitles corpora (Lison

Formality is a dataset where English queries contain the
pronoun “you”. In the target languages studied, “you” canbe formal or informal. As seen in the third example of table 1 where the target language x is French, the speakeraddresses the listener “you” as “Master Jedi” in the context,

1
Please note that due to the lack of large translation corpora
with various genders and the complexity in creating non-binary
gender datasets, our data is limited to feminine and masculine.

3Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

a title implying a formal style of politeness. The formality is ambiguous without the context and may impact thegenerated translation quality. Indeed, an incorrect choice informality level changes “vous serez” to “tu seras” and “cela”
to “ça”, decreasing BLEU scores by 58 points from 100.

canvasser method.
Q: Is “about” an adverb that means approximately, near ora preposition that means regarding, over, surrounding?
A: “about” means approximately.
Baselines. Our main baselines were chosen to compare
the cross-lingual generation abilities of large multipurpose
LMs given interaction, context or no additional information.
Please note that, to the best of our knowledge, there are noother baselines that (1) explore large multipurpose LM’scapability on contextualized (or interactive) multilingualtranslation; (2) do not require finetuning on large datasets.
LLM W C XT, our strongest baseline, is the only PaLMbased prompt method that benefits from having all of the
background information required to resolve ambiguities.
LLM W C XT has a prompt with exemplars formulated as theone in example 4.2. In the example, references to you andit are directly accessible in context C.
LLM NO E XTRA is a PaLM-based prompt method that
does not receive additional information to resolve ambiguities. This baseline is not only of interest for performancecomparison and to evaluate model bias but also it can provide insights on the usefulness of additional backgroundinformation to disambiguate queries. The structure of a
LLM NO E XTRA exemplar is similar to example 4.2 without
the context C. The model must translate the source sentence
S in the target language without knowing details about “it”
or the level of formality to employ for “you”.
GT RANSLATE is a commercially available multilingualand multipurpose baseline queried using the Google CloudTranslation API3 . This baseline allows us to set performance
expectations that LLM NO E XTRA model should reach.

Gender Neutral Names data includes queries where thename is gender neutral and ambiguous. The fourth example
in Table 1 shows a query where the name “Blair” is gender
neutral. In this dataset, we replace gendered pronouns in
the English query by the token [pr] to remove hints about
gender type. From the context, the speaker employs “her”
and we can infer that a feminine pronoun “ihr” should be
used in the translated German text.
Neutral Professions has 600 unique samples for two language pairs. This dataset is derived from the Translated
Wikipedia Biographies dataset2 that covers {en-de, en-es}.
In this dataset, the gender of typically gender-neutral professional designations is not clear from the English query
alone. In the fifth example of Table 1, the context provides
additional hints that the query is talking about “Margeret”,
also designated by the feminine pronoun “she”. Resolving
gender allows the model to correctly translate the list of professions in the query and potentially limiting the 70 points
drop in BLEU scores from 100.

4. Experimental Setup and Results
In this section, we present the main cross-lingual generation results of I NTER CP T for formality, “it” resolution andpolysemy ambiguity resolution subtasks.
Setup. We use PaLM (Chowdhery et al., 2022), a 540Bparameter decoder-only LM pretrained on primarily Englishcentric data with ∼20% of the data obtained from nonparallel multilingual corpora. The generalist prompt template is composed of two formality, three polysemy andthree “it” resolution exemplars. All prompt-based methods
are 8-shot with the same source sentences S to translate andcorresponding translated sentences A in the target language.
Each target language has its own prompt template since
A differs with every language. The simulated LM user isbased on a single English-only 8-shot prompt template forall target languages. Example 4.1 shows the structure of an
the LM user prompt exemplars for polysemy. A complete
overview of all prompts and exemplars used in experiments
can be found in Sections D.1 for the User LM and Sections D.2 for the generalist Translator LM.

Example 4.2. Given context (C), Translate (S) fromEnglish to French:
S: Are you sure that it is pretty?
C: She was trying on a new hat. Looking at herself in the
mirror, she asked her friend Isabelle.
A: Es-tu certaine qu’il est beau?
Metrics.

Our evaluation includes the standard BLEU and
BLEURT (Sellam et al., 2020) automatic translation quality

metrics as well as additional measures that assess specificambiguity resolution capabilities. For formality, we use arule-based classifier to quantify generated sentence formality levels (F-Acc) in the target language. We discuss detailsof the heuristics in Appendix E. Note that the formality
classifier is based on the formality data creation scripts thatallowed us to automatically identify formal and informal
sentences in the source corpus. For “it resolution”, we foundthat the PaLM 62B-parameter model was surprisingly accurate at identifying translated sentence genders (G-Acc).
As seen in Table 7 of Appendix E, PaLM 62B achieves

Example 4.1. Given a Context (C), provide an Answer (A)to the Question (Q):S: about
C: About 2% of the households are enumerated using the2
https://ai.googleblog.com/2021/06/a-dataset-for-studyinggender-bias-in.html

3

4

https://translate.google.ca/

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
Table 2. Translation results using an 8-shot generalist template that contains exemplars for formality, “it” resolution and polysemy
ambiguity types. F-Acc = formality accuracy, G-Acc = gender accuracy, B@n = BLEURT@n. BLEU and BLEURT results for I NTER CP T
labelled with † are significantly better than all other systems based on pair-wise significance testing (Koehn, 2004) with p = 0.05.
Lang.
Pairs
en→es

en→fr

en→de

en→ja

MethodI NTER CP T
LLM W C XTLLM NO E XTRAGT RANSLATEI NTER CP T
LLM W C XTLLM NO E XTRAGT RANSLATEI NTER CP T
LLM W C XTLLM NO E XTRAGT RANSLATEI NTER CP T
LLM W C XTLLM NO E XTRAGT RANSLATEFormality
BLEU

BLEURT

36.3†
34.7
34.6
31.4
39.1†
36.4
35.7
30.7
35.8†
33.6
32.5
27.5
28.6†
26.3
25.9
23.5

77.9†
77.1
77.0
75.3
70.6
69.9
69.2
67.4
75.0
74.6
74.4
72.3
69.7†
68.0
67.4
66.7

F-Acc.
67%
64%
62%
50%
72%
65%
63%
58%
69%
61%
62%
53%
67%
60%
61%
50%

“it” resolution
BLEURT
G-Acc.
33.6†
78.9†
77%
30.8
77.2
68%
29.6
75.9
63%
27.5
73.0
54%
35.3†
71.7†
73%
33.5
68.4
68%
32.3
66.7
66%
29.1
65.4
61%
24.0†
76.0
75%
22.4
75.0
69%
22.8
73.2
63%
22.1
73.0
59%
23.1†
72.4†
74%
21.4
70.8
67%
21.2
70.3
61%
19.9
68.6
52%

BLEU

97% and 93% accuracy in classifying samples of generated translations for Spanish and French respectively. For
polysemy, we found that exact match metrics did not fully
describe the performance of models. Whenever the model
generated a synonym of the ground truth, the exact match
metric would not consider the prediction correct. The LLMNO E XTRA polysemy exemplars are a comma-separated listof synonyms. Our hit@n measures whether the groundtruth exists in the first n generated words. For example, if
the model outputs the list of Spanish words [“aproximadamente”, “cerca de”, “alrededor de”, “casi”, “más o menos”],
for n = 3, hit@3 would return a match for a ground truth target “cerca de” and no-match for a ground truth target “casi”.
To supplement the hit@n metric, we also report results of a
new metric that we call BLEURT@n (B@n) which returns
the highest BLEURT score of the first n generated wordphrases. Since BLEURT captures the non-trivial semantic
similarities between words using its contextual representations from BERT, we found that the metric better measures
if correct synonyms were generated by the model. Notethat we did not report the GT RANSLATE hit@n or B@n
numbers since the API only provides single word outputs.

Hit@3
46%
40%
33%
—
46%
36%
33%
—
43%
35%
32%
—
41%
34%
30%
—

Polysemy
Hit@10 B@3
48%
54.6†
46%
46.9
40%
44.9
—
—
48%
46.9†
40%
40.1
37%
38.1
—
—
45%
45.1†
39%
36.1
35%
36.7
—
—
44%
44.7†
38%
35.8
33%
34.6
—
—

B@10
56.8†
55.1
51.0
—
48.5†
44.7
41.8
—
47.6†
44.9
41.3
—
47.0†
43.8
37.0
—

closer to the ground truth while conserving the correct semantics.

5. Analysis
Table 3. Translation results on unseen ambiguity subproblems
using the Gender Neutral Names data and with added unseen
domain using the Neutral Professions data. I NTER CP T resultslabelled with † are significantly better with p = 0.05.
MethodBLEU BLEURT
G-Acc.
Gender Neutral Names — unseen ambiguities
I NTER CP T
31.8†
74.1†
76%
LLM W C XT
29.9
72.4
66%
en→es
LLM NO E XTRA 30.9
71.6
59%
GT RANSLATE
27.8
66.1
56%
I NTER CP T
31.0
63.5†
71%
LLM W C XT
29.5
62.6
64%
en→fr
LLM NO E XTRA 30.0
60.9
63%
GT RANSLATE
24.5
57.7
56%
I NTER CP T
17.9†
72.2
73%
LLM W C XT
15.6
71.5
67%
en→de
LLM NO E XTRA 15.2
70.8
61%
GT RANSLATE
17.1
67.1
55%
I NTER CP T
16.1†
70.3†
71%
LLM W C XT
14.7
69.1
65%
en→ja
LLM NO E XTRA 14.4
68.3
60%
GT RANSLATE
14.1
66.0
54%
Neutral Professions — unseen ambiguities + unseen domain
I NTER CP T
37.3
75.8
70%
LLM W C XT
37.1
76.1
69%
en→es
LLM NO E XTRA 35.5
75.7
59%
GT RANSLATE
37.0
72.7
56%
I NTER CP T
14.3
70.0
68%
LLM W C XT
14.0
71.9
66%
en→de
LLM NO E XTRA 12.2
70.0
62%
GT RANSLATE
13.8
67.2
54%
Pair

Discussion. Our test results for en-es, en-fr, en-de and enja are summarized in Table 2. We first notice that I NTER CP T
surpasses all other baselines. Surprisingly, LLM W C XT,even with all the necessary background to resolve ambiguities, significantly lags behind I NTER CP T on F-Acc. forformality, G-Acc. for “it resolution” and both hit@3 andB@3 for polysemy. This results suggests that the multistep
computation approach of fist resolving the ambiguity subproblems and then generating text has an advantage overother baselines. BLEU scores are also 2-3 points higher
while BLEURT scores are only slightly higher. This suggest that I NTER CP T generates sentences syntactically muchHere, we provide key insights on I NTER CP T. We show that
I NTER CP T better generalizes to unseen ambiguities in Sub5

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
Polysemy (Hit@3) "it" resolution (BLEU) Formality (BLEU)

ES
30
25
20

8

62

30
InterCPt
LLMnoExtra
20
LLMwCxt
540
8

DE
30

62

InterCPt
LLMnoExtra 20
LLMwCxt
540
8

30

30
25
20

FR

40

35

62

540

25
InterCPt
LLMnoExtra 20
LLMwCxt
540
8

62

540

8

62

540

40

40

40

40

30

30

30

30

20
8

62

540

20

8

62

540

20

8

62

540

8

62

540

15

15
8

62

InterCPt
LLMnoExtra
LLMwCxt
540

20

20

20
8

62

JA

8

62

540

20

Translator Model scale (# parameters in billions)Figure 3. I NTER CP T enables large LMs to solve ambiguity subproblems in cross-lingual generation. The multistep disambiguate-translate
capability is an emergent ability that is reached at higher parameter scales.

45 Formality (BLEU)
40353025 ES FR DE JA

section 5.1. In Subsection 5.2, we see that templates with
examplars covering many ambiguities (i.e. generalist) performs on par with ones with single ambiguity examplars (i.e.
specialist). In Subsection 5.3, we find that interactive translation is an ability that emerges with scale. Subsection 5.4
shows that User LM scale is important, with best scores
reached with the 540B-LM model. We study our method’s
failure modes in Subsection 5.5 showing that where we can
improve I NTER CP T further. Finally, we provide evidence
that interaction helps better mitigate bias in Subsection 5.6.

50 Polysemy (Hit@3)
45
40
35 ES

5.1. How does interaction generalize?

FR

DE

JA

80 Formality (F-Acc.)
75
70
65
60 ES FR DE JA

Generalist
Specialist

60 Polysemy (B@3)
55504540 ES FR DE JA

Figure 4. Generalist vs Specialist prompt templates for Spanish(ES), French (FR), German (DE) and Japanese (JA) targets.

In Table 3, we provide translation test results on two heldout datasets that are described in Section 3: (1) Gender
Neutral Names and (2) Neutral Professions. We use thesame generalist prompt template as in Section 4 with exemplars that cover only formality, “it” resolution and polysemy.
Specifically, our exemplars for both the Translator LM andthe User LM do not contain exemplars to resolve the gender for a person’s name or profession. We observe thaton the Gender Neutral Names dataset I NTER CP T performs
best on BLEU and BLEURT and is much more able to resolve ambiguities with 6 to 10 points G-Acc improvements
over LLM W C XT. On the Neutral Professions data, where
test samples are taken from a different domain (Wikipedia
biographies instead movie scripts), LLM W C XT and I N TER CP T have similar performances. It is possible that
LLM W C XT benefits from additional sentences in the context to better determine the style of the output. Nonetheless,
I NTER CP T provides a 1-2 point increase on G-Acc.

exemplars per ambiguity. In Fig. 4, we present results of specialist template that only covers one type ambiguity at thetime (either all formality or all polysemy). Interestingly, specialization does not seem to provide much additional benefit
in resolving ambiguities as evidenced by F-Acc, Hit@3 andB@3 results that are on par and often lower than the generalist approach. However, the specialist template does havea higher BLEU score, implying greater syntactic alignment
with the target translation when more ambiguity-specificexemplars are added.
5.3. Are interactive generation abilities emergent?
We show in Fig. 3 for each prompt template the effects ofscaling PaLM parameters on the performance of formality,“it” resolution and polysemy for Spanish (ES), French (FR),German (DE) and Japanese (JA) target languages. Please
note that while we vary the parameter count (8B, 62B and
540B) of the Translator LM, the User LM is a 540B parameters PaLM model for all experiments. The plots provide

5.2. Are specialist better than generalist prompts?
So far, we have studied a generalist 8-shot template covering three different types of ambiguities with at most three6Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
40
35
30
25
20
15
10

Formality (BLEU)

ES

FR

DE

JA

"it" resolution (BLEU)

40
35
30
25
20
15
10

Table 4. Examples of interaction chain errors.

8B
62B
540B

ES

FR

DE

Error
Type

Wrong
S: But I swear to C: I just thought that S can be translated withQuestion you it wasn’t me. he’d blame me for pre- out information on “it”
Q: What does “it” dicting his death [...].
and did ask a question torefer to?
A: “it” is death
disambiguate formality.
Wrong
S: Develop it fur- C: -Get me a complete “plan” is masculine in fr
Answer ther, Leonard.
rundown on Miller [...]. - and es. However, “it”
Q: What does “it” That’s a good idea.
refers to “idea”, which isrefer to?
A: “it” is a plan
feminine in fr and es.
Many
S: If anyone asks, C: -Okay, so I’m going The answer is correct
Ambigui- you’re a relief to go with you. -White however the nameties
worker.
girls don’t do runs.
Aaron is gender neuQ: Who does A: ’informal’ since the tral and was resolved“you” refer to?
speaker talking to a incorrectly, impacting
friend “Aaron”“worker” translation.
Limited S: I’ll bring it C: -You didn’t get it? - “harp” is likely wrong.
Context right over.
Really? -Just a second... We cannot determine
Q: What does “it” A: “it” is a harp
what “it” is from therefer to?
given context.

JA

Figure 5. Scaling Simulated User LM improves the performanceof a 62B Translator LM model.

interesting insights. First, at the 8B parameter scale, LLMNO E XTRA performs best across all languages for Formalityand “it” resolution across all language pairs. Neither context or interaction seem to provide benefits to translation.
Second, at the 62B parameter scale, the LLM W C XT andI NTER CP T methods have on par performances. Context
or interaction in this case are only clearly beneficial forpolysemy. Third, the PaLM 540B parameter I NTER CP T
outpaces other prompt-based methods across language pairs
and ambiguity subproblems. At this stage, baselines scaling
trend decelerates, with scaling curves flattening, comparedto I NTER CP T. It shows that I NTER CP T is an emergent ability of model scale (Wei et al., 2022a). We conjecture that the
emergent behavior of I NTER CP T is due to a better ability
to ask questions and incorporate answers before generating
final prediction.

In this section, we provide analysis that describes common areas of improvement for generalist interactive-chain
prompting. We first isolated test samples for French andSpanish for four ambiguities (formality, “it” resolution,neutral professions and gender neutral names) where the
BLEURT scores were less than or equal to LLM W C XT
scores. We then randomly sampled 50 interactions andmanually analysed the interaction chains (query, question,
context, answer, translation). This led us to five types oferrors: (1) wrong question, when the Translator LM asked
a question not related to the ambiguity; (2) wrong answer,
when the User LM did not provide correctly disambiguate;(3) many ambiguities, when the query had multiple unresolved ambiguities or the User LM answer also contained
ambiguities; (4) limited context, when the context was not
sufficiently informative to resolve ambiguities; (4) style orother, when generated translated text had discernible differences with the ground truth. Fig. 6 shows that the majorityof errors are from wrong User LM answers for formalityand “it” resolution. This partially confirms our hypothesis
in Subsection 5.4. For tasks involving unseen ambiguities,
the majority of errors come from the Translator LM with
68% to 78% of sample chains having the wrong question or
noticeable differences in generated translated text style orform. We provide examples of interaction chains for eachtype of error in Table 4.

5.4. How important is User LM parameter scale?
While the User LM allows us to automate the evaluation ofinteractivity for cross-lingual generation, it is not clear if thequality of the answer to the Translator LM questions impact
performance. We hypothesize that a larger User LM model
would provide higher quality answers and allow the Translator LM to better generate translated text. Fig. 5 shows that,when the Translator LM is a 62B PaLM model, a higher
parameter User LM improve overall performance. It is therefore possible that answer quality has a significant impact ontranslation quality and that human-generated answers canfurther improve overall performance.
5.5. When is context better than interaction?
Wrong Question
Wrong Answer
Formality (FR)Formality (ES) 2%"it" rez (FR) 2%"it" rez (ES)

34%
38%

Many Ambiguities
Limited Context
14%

42%

32%

16%

32%

12%

40%

22%
18%

Style or Other

20%
6%
4%

5.6. Can interaction help solve NLG bias issues?
Gender bias is a common phenomenon in automated NMT
systems (Borkan et al., 2019; Stanovsky et al., 2019; Saunders & Byrne, 2020). Even when there are explicit genderpronouns in the input query or in the context, NMT systems
generated text tends to be masculine when translated into
languages with grammatical gender (Stanovsky et al., 2019;
Saunders & Byrne, 2020; Stafanovičs et al., 2020; Wanget al., 2022). To measure gender bias, all generated trans-

30%
36%

Prof. (ES)

34%

24%

2%6%

34%

Names (ES)

38%

20%

2%

40%

en Query (S) and Sim User Context (C) Observation
Question (Q)and Answer (A)Figure 6. Error analysis. rez = “it” resolution, Prof. = Neutral
profession, Names = Gender Neutral Names

7

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
feminine
Data

50%Interactive

45%

With context 24%
No extras 19%
Google Translate8%

masculine
50%
55%
76%

informal
51%

formaluser in the target language. Our approach discovers preferences and background knowledge about an input query inthe source language and more flexibly adapts translations
according to a user’s natural language response. The interaction is similar to Conversational AI systems where user
utterances influence generated outputs. Task or goal oriented conversational AI systems (Konstantinova & Orasan,
2013; Gao et al., 2018; Hussain et al., 2019) are typically
deployed to answer knowledge-based questions, seek information or solve basic queries (e.g. making reservations,purchase an item). To the best of our knowledge, our work is
the first to explore conversational interaction in cross-lingual
generation.

49%

46%

54%

41%

59%

81%

39%

61%

92%

34%

66%

Figure 7. Bias in generated translations for French and Spanishon “it” resolution (left) and formality (right).

lations are passed through the gender classifier for the “it”
resolution balanced dataset. Similarly, to measure formality
bias, generated translations are passed through the formality
classifier for the formality balanced dataset. NMT systems
can also suffer from formality bias (Rippeth et al., 2022).
However, we notice that I NTER CP T is much closer to evenly
producing masculine and feminine sentences. Our resultsshows that interactive ambiguity resolution via multistep
computation better addresses gender and formality biases.

Resolving ambiguities by asking for clarifications has
been a recent topic of research, for QA and conversational
search systems (Lee et al., 2019; Aliannejadi et al., 2019; Zamani et al., 2020; Dhole, 2020; Wang & Li, 2021; Wu et al.,2022b). Departing from such methods, I NTER CP T does
not produce sentences from a preset list of questions but isgenerated from a large LM without constrain. Concurrently
to our work, Krasheninnikov et al. (2022) explored finetuning GPT-3 to generate clarifying questions and provide
answers using human generated data from AmbigQA (Minet al., 2020) for open-domain QA. Another GPT-3 model
simulates the user and generates answers while conditioned
on ground-truth clarification questions. In contrast, ourprompt-based method only needs few-shot demonstrations.
Further, our simulated user does not rely on ground-truth
clarification questions to provide an answer, which could be
more realistic for a number of applications (including QA,
text simplication, code generation).

6. Related Works
Prompting for Cross-Lingual Generation using Large
LMs is a technique that has garnered increasing attentionof late. Works on GPT-3 (Vaswani et al., 2017) and PaLM
(Chowdhery et al., 2022) show competitive n-shot BLEU
translation results on WMT. The prompt demonstrations
are populated with n random sentence pairs taken fromthe WMT training corpora and evaluated on the test corpora at inference. Orthogonal to our work, POMP (Vilar
et al., 2022) improves upon this PaLM-based prompting
technique by explicitly optimizing for the selection of ndemonstration sentence pairs and obtaining results competitive with the state-of-the-art. More recent work (Garcia& Firat, 2022) using mT5 (Xue et al., 2021) investigatedadding prompt-based natural language specifications to influence translated text properties such as formality level ordialect type. Experiments show that prepending textual artifacts such as “your majesty” to the English query conditions
mT5 to generate translations in a formal tone. Our work
prompts PaLM with n random translation pair exemplars
as well. Different from previous research, we prompt with
exemplars to interactively discover background knowledge
or clarify ambiguities before translating.

7. Conclusion
We propose interactive-chain prompting (I NTER CP T), a
prompt-based interactive multistep computation techniquethat first resolves cross-lingual ambiguities in the input
queries and then performs conditional text generation. We
have created and released a new datasets that covers five
ambiguities: formality, “it” resolution, polysemy, gender
neutral names and neutral professions for four different
language pairs. Empirical results show that I NTER CP T outperforms other prompt-based techniques that have access toall background information and context to directly resolve
ambiguities. We find that I NTER CP T MT is an emergent
property of parameter scale that allows Large LMs to perform interactive generation tasks while other prompt-based
techniques exhibit flattening scaling curves. I NTER CP T canbe considered a step forward more efficiently interactingwith machine learning systems.

Interactive Machine Learning (Ware et al., 2001; Fails& Olsen, 2003; Amershi et al., 2014) is an approach where
information is interactively and iteratively supplied to alearning system. In prior interactive translation work, machine interactivity has assisted translators in writing translations by displaying automated word suggestions that update incrementally (Green et al., 2014; Santy et al., 2019).
The approach however is limited by drop-down menu options and requires a certain level of sophistication from the8Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

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12Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

A. More details on I NTER CP T interactive steps
To make link between interaction steps in Figure 1, the process overview in Section 2, the appendix code and templates, we
add the following:
Step 1: The Translation LM asks a question on ambiguity using language specific methods in Appendix D.2. It takes as input
the English text to Translate en text and outputs the question Q. For example, if we want to translate English to Spanish
with a generalist template, we can use spanish generalist translator interactive(...).
Step 2: The User LM answers the question Q generated in step 1 using any method in Appendix D.1. It takes as input en text
and the context C (ctx in the code) and outputs the answer U . For example, we can use generalist simulated user context(...).
Step 3: If no other ambiguity is detected, the Translation LM translates using language specific methods in Appendix D.2. It
takes as input the English text to Translate en text, the question Q, and the answer U and outputs the translation A.

B. Link between Chain-of-Thought and Least-to-Most prompting
In this section, we add a few more words on the link between I NTER CP T, Chain-of-Thought (CoT) and Least-to-Most(L2M) prompting. CoT performs better than the baseline that has access to the whole information in the problem statement(similar to having context). The behavior is attributed to the sequential solving of subproblems (in our case ambiguity) and amultistep computation (in our case interaction). LLM W C XT has access to more information but does not involve multiple
computation steps to solve a subproblem while I NTER CP T does

C. More details on A MBIG MT ambiguity datasets
In this section, we provide additional information on what the datasets contain and how they were created. As mentioned in
Section 1, to the best of our knowledge, datasets that cover a large set of ambiguities for multiple language pairs do not exist.
We provide an overview of publicly available datasets in Table 5. Upon manual inspection of samples from other public
datasets, we found that translation queries were often (> 50%) unambiguous since the translation query contained enoughinformation and did not need to rely on the provided context. We inspected 200 samples from A MBIG MT and found that
only 3% of queries did not need context to disambiguate the linguistic phenomena.

Table 5. Other MT datasets that contain specific linguistic phenomena and provide context.
en = English, de = German, fr = French, ru = Russian, zh = Mandarin Chinese, ja = Japanese.
Dataset Source

Language Pairs

Linguistic PhenomenaMüller et al.
Bawden et al.
Voita et al.

en→de
en→fr
en→ru
de→en
zh→en
en→ru
en→es
en→fr
en→de
en→ja

(1) “it” pronoun resolution(1) Anaphora resolution, (2) lexical cohesion(1) Ellipsis, (2) lexical cohesion

12,000900
6,000(1) “it” pronoun resolution, (2) lexical cohesion

6,090

Voita et al.

A MBIG MT (ours)

Total Test Data Size

(1) “it” pronoun resolution, (2) gender neutral names
17,200(3) neutral professions, (4) polysemy, (5) formality

C.1. Dataset statistics
We present in Table 6 the data statistics for A MBIG MT. For polysemy, the total senses per word is the number ofdifferent definitions or meanings found for a specific source English word. Each ambiguity is well balanced acrossclasses formal/informal or feminine/masculine. The Neutral Professions dataset is derived from the Translated Wikipedia
Biographies dataset4 that only covers {en-es, en-de} language pairs.
4

https://ai.googleblog.com/2021/06/a-dataset-for-studying-gender-bias-in.html

13Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
Table 6. A MBIG MT data statistics of each type of class and language pair.
Form = formal, Inform = informal, Mas = Masculine, Fem = Feminine, res = resolution, Prof = Profession.

Language
Pair

Total
ExamplesPolysemy
Senses/Word

en→es
en→de
en→fr
en→ja

4600
4600
4000
4000

3.6
3.1
3.3
3.0

Formality
Form. Inform.

“it” res.
Mas. Fem.

Neutral Names
Mas.
Fem.

Neutral Prof.
Mas. Fem.

49%
50%
49%
50%

50%
52%
50%
52%

51%
50%
51%
53%

52%
53%
—
—

51 %
50 %
51 %
50 %

50%
48%
50%
48%

49%
50%
49%
47%

48%
47%
—
—

C.2. A MBIG MT data creation tools, process and heuristics
In this section, we present the steps, tools and heuristics used to detect ambiguities. For polysemy, formality, “it” resolution,
gender neutral names, we extract the data from OpenSubtitles corpora and neutral professions from Translated Wikipedia
Biographies. The source data that was used consists of parallel sentence level pairs. We first detect a sentence that has a
specific ambiguity and extract the context by taking three to five preceding English sentences, depending on sentence size.
For Polysemy, the context is an English sentence that contains the polysemous word that will be translated. The code anddatasets are released here.
C.2.1. P OLYSEMYWe provide the following list of steps to create the polysemy dataset for all languages:
1. Extract polysemous words from Wordnet. (Miller, 1994) using the NLTK toolkit (Bird & Loper, 2004)5 .
• Create a list of English words.
• Compute the number of definitions per word without counting definitions with synonym overlap.
• Extract polysemous words (we ) with more than three definitions and a word length greater than four.
2. For each Polysemous English word we , extract a list lx = {wx1 , . . . , wxN } of possible word translations using theGoogle Cloud Translation v2 API, where x ∈ {es, fr, de, ja} is the target language.
3. For each Polysemous English word we and each target language x ∈ {es, fr, de, ja}:
• Find a sentence that contains the word we in the OpenSubtitle dataset.
• If the parallel sentence contains one of the translated word wxi ∈ lx from step 2 and no other translated word, keep
the English sentence as context.
C.2.2. F ORMALITY
Each language has specific formality rules. For Japanese, we direct the reader to our public code: https://anonymous.
4open.science/r/interactive_chain_prompting. We provide the following list of steps to create the formality dataset for Spanish, French and German:
1. Find a sentence that contains “you” or “your” and that has word count less than 20, in the English OpenSubtitle corpus.
2. Select parallel sentences for each target language x ∈ {es, fr, de, ja} that meet the following criteria.
3. If x == es, check the following in parallel Spanish sentence (all checks are initialized to FALSE):• If all verbs finish by “s”, “ste” or “os”, then is verb informal = TRUE.
• If any pronouns is “usted”, then is pronoun formal = TRUE.
• If any pronouns is in [“tú”,“tu”,“te”, “vos”, “vosotros”], then is pronoun informal = TRUE.
• If any determinants is “su”, then is determinant formal = TRUE.
• If any determinants is in [“tu”,“vosotros”, “vosotras”] then is determinant informal = TRUE.
• is informal = is verb informal and is pronoun informal and is determinant informal.
• is formal = is pronoun formal and is determinant formal.
4. If x == fr, check the following in parallel French sentence (all checks are initialized to FALSE):
5

See example in https://www.nltk.org/howto/wsd.html

14Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction• If any verbs finish by “x”, “s” or “ons”, then is verb informal = TRUE.
• If any verbs finish by “ez”, then is verb formal = TRUE.
• If one of the pronouns is “vous”, then is pronoun formal = TRUE.
• If one of the pronouns is “tu”, then is pronoun informal = TRUE.
• If one of the determinants is in [“vos”,“votre”], then is determinant formal = TRUE.
• If one of the determinants is in [“tes”,“ton”, “ta”, “toi”] then is determinant informal = TRUE.
• is informal = is verb informal and is pronoun informal and is determinant informal.
• is formal = is verb formal and is pronoun formal and is determinant formal.
5. If x == de, check the following in parallel German sentence (all checks are initialized to FALSE):• If “!” not in sentence and one of the pronouns is in [“Sie”,“Ihr”, “Ihre”, “Ihren”, “Ihrem”, “Ihrer”, “Ihres”], thenis pronoun formal = TRUE.
• If one of the pronouns is in [“du”,“dein”, “deine”, “deinen”, “deinem”, “deiner”, “deines”, “dich”], thenis pronoun formal = TRUE.
• If “!” in sentence one of the pronouns is in [“er”,“sie”, “es”, “ihr”], then is pronoun formal = TRUE.
• is informal = is pronoun informal.
• is formal = is pronoun formal.
6. Keep samples if is formal != is informal, use ‘formal’ label if is formal or ‘informal’ label if is informal.
7. For each sample, create context by keeping the preceding three to five English sentences, depending if word count isabove 20.
C.2.3. “ IT ” RESOLUTION
We provide the following list of steps to create the “it” resolution dataset. The steps apply to all languages:
1. For each English sentence in the OpenSubtitle dataset, keep sentences where the word“it” exists.
• Using a dependency parser, if “it” is expletive6 , skip sample.
• In the parallel Spanish, French, German or Japanese sentence, if the sentence does not contain a verb and a gendered
pronouns, skip sample.
• Keep gender label.
2. For each sample, create context by keeping the preceding three to five English sentences, depending if word count isabove 20.
C.2.4. G ENDER N EUTRAL NAMES
We provide the following list of steps to create the gender neutral names dataset. Please note that for simplicity we usedbinary genders. Genders beyond female and male will be left for future work. The steps apply to all languages:
1. Compile a list Lgnn of gender neutral (unisex) names• Collect a list of names with gender statistic such as the percentage of people with the name who identify as female
or male7 .
• Keep the names that are used in approximately equal proportions (unisex) with at least a female or male proportion
above 40%.
2. For each gender neutral name ∈ Lgnn , find a sentence that contains the name in the English sentence and keep thecorresponding parallel sentence in Spanish, French, German or Japanese.
• If the English sentence has gendered pronouns, skip the sentence if multiple genders are detected.
• If the English sentence has no gendered pronouns, use a Part-of-Speech tagger8 on the corresponding parallel
sentence in Spanish, French, German or Japanese and skip the sentence if multiple genders are detected.
• Keep gender label.
3. Replace gendered pronouns with [pr] in the source English sentence to remove simple clues about the name’s gender.
6

The spaCy dependency parser can be used to find expletive “it”.
Names with gender statistics were compiled and combined using a Japanese names database (Ogihara, 2020) and a English names
database that originates from the United States Social Security Administration.
8
Language specific spaCy models could be used.
715

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

4. For each sample, create context by keeping the succeeding three to five English sentences, depending if word count isabove 20.

D. Prompt templates used in experiments
In this section, we discuss the main prompt templates used in experiments. This includes I NTER CP T Translator generalist
and specialist templates to ask questions about ambiguities and exemplars to translate in French, Spanish, German orJapanese. It also includes I NTER CP T User generalist and specialist templates to answer questions given a context. We also
provide the prompt templates for the LLM W C XT experiments where we use context and the same exemplars to translate inFrench, Spanish, German or Japanese. Please note that we have normalized special characters for simplicity. The German
and Japanese templates as well as Spanish and French templates with special characters can be found in our public code anddata repository. In the python methods listed below, en text is the input query, ctx is the context, question is the question
from the Translator model and anwer is the answer from the User model.
D.1. I NTER CP T Simulated User Prompts
The 8-shot generalist Simulated User prompt template is the same for all languages and is provided in code block listing 1.
1
2

def generalist_simulated_user_context(en_text, question, ctx):"""Generalist Simulated user has access to context and answers the question."""3
4
5

templated_input =f"""[web] Given a Context (C), provide an Answer (A) to the Question (Q):6

S: about
C: About 2% of the households are enumerated using the canvasser method.
9 Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?
10 A: "about" means approximately.
7
8

11
12

S: rent
C: Many single women cannot live independently because they cannot (afford to) own or rent
housing
15 Q: Is "rent" a tenant’s regular payment for a property or to pay someone for the use ofsomething?
16 A: "rent" is to pay someone for the use of something.
13
14

17
18

S: abstract
C: For the international community is not an abstract concept, it consists of us ourselves.
21 Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?
22 A: "abstract" is an adjective that modifies "concept" in the phrase "abstract concept".
19
20

23
24

S: What do you mean?
C: Daria, I just think that your field of vision could really be enhanced... - Come on,
Mom. - It’s not my field of vision you want to enhance.
27 Q: "you" can be neutral, formal, informal. Who does "you" refer to?
28 A: "you" is ’informal’ since the listener is the speaker’s "mom", it implies a familiarity
with the listener "you".
25
26

29
30

S: This will accelerate your metabolic functions-- help you make the transition.
C: At the very least, get them to hold their fire. - Captain, the transporters are offline. The docking port hasn’t been hit yet.
33 Q: "you" can be neutral, formal, informal. Who does "you" refer to?
34 A: "you" is ’formal’ since "you" refers to a Captain and the speaker will typically use apolite form.
31
32

35

16

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
36

S: You know where it begins, you never know where it ends...
C: Someone once told me we always are where we’re supposed to be. - Now I believe it. Life is a journey.
39 Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
40 A: "you" is \’neutral\’ because it is a generic "you" that refers to people in general on
their journey through life.
37
38

41
42

S: it is also very pretty.
C: Even when it is pouring outside, this umbrella is both practical and elegant.
45 Q: What does "it" refer to?
46 A: "it" is an umbrella.
43
44

47
48

S: Tell me, why do they have to tilt it?
C: -Frog is wrong. - I see here that you play the harp.
51 Q: What does "it" refer to?
52 A: "it" is a harp.
49
50

53
54

S: {en_text.strip()}
C: {ctx.strip()}
57 Q: {question}
58 A:"""
59
return templated_input
55
56

Listing 1. I NTER CP T Generalist Simulated User Prompt Template

The 8-shot formality specialist Simulated User prompt template is the same for all languages and is provided in code blocklisting 2.
1
2

def formality_simulated_user_context(en_text, question, ctx):"""Formality simulated user has access to context and answers the question."""3
4
5

templated_input =f"""[web] Given a Context (C), provide an Answer (A) to the Question (Q) about Sentence (S):

6

S: This is for you, too.
C: I’m Freya. - Welcome to Denmark, Mr. Helm. - You always greet people like this? - I’m
Freya Carlson, your Tourist Bureau contact.
9 Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
10 A: "you" is \’formal\’ since "you" refers to a customer or tourist that Freya Carlson isgreeting with the polite form "Mr.".
7
8

11
12

S: - i can gladly help you.
C: I will go to town to fetch the materials. Once I return, we can repair your majesty’s
royal carriage.
15 Q: "you" can be formal or informal. Who does "you" refer to?
16 A: "you" is \’formal\’ since "you" refers to "your majesty".
13
14

17
18

S: You know what I mean.
C: Elizabeth, will you bring the binoculars? - [Elizabeth] Mm, the stench is horrible. [John] Here, take a hold of this. - [Elizabeth] Is it dead?
21 Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
22 A: "you" is \’informal\’ since the listener "John" has familiarity with the speaker anduses the first name "Elizabeth".
19
20

23
24

S: You think you can make it through that kind of stuff, you think you can make it throughanything.
26 C: Well, transitions are hard. - Been together ever since college. - Been through a lot. 25

17

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
You know, us coming out to her family, and her brother dying.
Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
28 A: "you" is \’neutral\’ because it is a generic "you" that refers to people in generalgoing through a difficult moment.
27

29
30

S: You can imagine the princess-sized tantrum that followed.
Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
33 C: This is the bike that I learned to ride on. - I just didn’t know my mom kept it. - It
used to have these training wheels on the back with lights that would flash every time
you pedaled. - Then one day, my mom took them off and said it was time to be a big
girl.
34 A: "you" is \’informal\’ since the speaker is talking about a funny childhood memory which
implies a familiarity with the listener "you".
31
32

35
36

S: Can I just say, it’s been an absolute pleasure to finally meet you?
C: Generations of Daleks just woke up very cross, and they’re coming up the pipes. - Or to
put it another way... bye! - Doctor, you must help me.
39 Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
40 A: "you" is \’formal\’ since "you" refers to a "Doctor" that the speaker just met.
37
38

41
42

S: You know where it begins, you never know where it ends...
C: Someone once told me we always are where we’re supposed to be. - Now I believe it. Life is a journey.
45 Q: "you" can be neutral, formal, informal. Who does "you" refer to in (S)?
46 A: "you" is \’neutral\’ because it is a generic "you" that refers to people in general on
their journey through life.
43
44

47
48

S: City policemen questioned many of you this week.
C: Lying on his belly, he was carried home on a makeshift stretcher. - Next Sunday, after
the service, the Baron asked the pastor to let him speak.
51 Q: "you" can be neutral, formal, informal. Who does \"you\" refer to in (S)?
52 A: "you" is \’formal\’ since the speaker directly addresses several people or "many of you", the plural form of "you".
49
50

53
54

S: {en_text.strip()}
C: {ctx.strip()}
57 Q: {question}
58 A: """
59
return templated_input
55
56

Listing 2. I NTER CP T Formality Specialist Simulated User Prompt Template

The 8-shot polysemy specialist Simulated User prompt template is the same for all languages and is provided in code blocklisting 3.
1
2

def polysemy_simulated_user_context(en_text, question, ctx):"""Polysemy simulated user has access to context and answers the question."""3
4
5

templated_input =f"""[web] Given a Context (C), provide an Answer (A) to the Question (Q):6

S: abstract
C: For the international community is not an abstract concept, it consists of us ourselves.
9 Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?
10 A: "abstract" is an adjective that modifies the word "concept".
7
8

11
12
13

S: abstract

18

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
C: We need to abstract the data from various studies.
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?
16 A: "abstract" means to extract something.
14
15

17
18

S: about
C: About 2% of the households are enumerated using the canvasser method.
21 Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?
22 A: "about" means approximately.
19
20

23
24

S: about
C: The story is about soldier returning home after the war.
27 Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?
28 A: "about" means regarding.
25
26

29
30

S: bank
C: The online banking application does not work. I tried a few times and I could nottransfer the funds. I went to the bank.
33 Q: Is "bank" a financial institution, the edge of a river, a set or series of similar
things or the cushion of a pool?
34 A: "bank" is a financial institution.
31
32

35
36

S: rent
C: Many single women cannot live independently because they cannot (afford to) own or rent
housing
39 Q: Is "rent" a tenant’s regular payment for a property or to pay someone for the use ofsomething?
40 A: "rent" is to pay someone for the use of something.
37
38

41
42

S: bat
C: The bat flew over the forest and back to its cave.
45 Q: Is "bat" an animal or a sports equipment?
46 A: "bat" is an animal.
43
44

47
48

C: {ctx}
Q: {question}
51 A: """
52
return templated_input
49
50

Listing 3. I NTER CP T Polysemy Specialist Simulated User Prompt Template

D.2. I NTER CP T Generalist Prompt Templates for each target language
The 8-shot Spanish generalist Translator prompt template is the same for all test ambiguity data and is provided in code
block listing 4.
def spanish_generalist_translator_interactive(en_text, question=None, answer=None):"""Translation model asks questions and uses answers to translate"""3
if answer == None:4# Ask questions
5
instructions = "[web] Given sentence ’S’ to translate to Spanish, ask clarifying
questions ’Q’ to clarify ambiguities or multiple senses:"
6
else:
7
# Translate given answer
8
instructions = "[web] Given answer ’U’ to question ’Q’, provide the Spanishtranslation ’A’ of sentence ’S’. Provide the best answer:"
1
2

19

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
9

templated_input =

10
11

"""

12
13
14

S: about
Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?%s

15
16
17
18

S: rent
Q: Is "rent" a tenant’s regular payment for a property or to pay someone for the use ofsomething?%s

19
20
21
22

S: abstract
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?%s

23
24
25
26

S: You think if I get contacts I’ll suddenly turn into the homecoming queen.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

27
28
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30

S: This will accelerate your metabolic functions-- help you make the transition.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

31
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S: They could wait ’till you’re on the beach, then cut loose, or start firing right away.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

35
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38

S: can’t they just build it on an angle?
Q: What does "it" refer to?%s

39
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42

S: It is also very pretty.
Q: What does "it" refer to?%s

43
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"""
if answer is None:
templated_input = templated_input % (’’, ’’, ’’, ’’, ’’, ’’, ’’, ’’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ:"else:
templated_input = templated_input % (’\nU: "about" means approximately.\nA: aproximadamente, cerca de, alrededor de, casi, mas o menos’,’\nU: "rent" is to pay someone for the use of something.\nA: alquilar,
arrendar, rentar’,’\nU: "abstract" is an adjective that modifies "concept" in the phrase "abstract concept".\nA: abstraccion, abstracto’,’\nU: "you" is \’informal\’ since the listener is the speaker\’s "mom", it
implies a familiarity with the listener "you".\nA: Tu piensas que si uso lentes decontacto de repente me convertire en la nueva reina del colegio.’,’\nU: "you" is \’formal\’ since "you" refers to a Captain and the speaker will
typically use a polite form.\nA: Esto acelerara sus funciones metabolicas. Lo ayudara
a hacer la transicion.’,’\nU: "you" is \’neutral\’ because it is a generic "you" that refers to people
in general and not someone specific.\nA: Podian aguardar a que uno estuviera en la
playa y atacar o comenzar a disparar.’,’\nU: "it" is a harp.\nA: no pueden hacerla en angulo?’,’\nU: "it" is an umbrella.\nA: Es muy bonita tambien.’,)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: {question}\nU: {answer}\nA: "20

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
return templated_input

61

Listing 4. I NTER CP T Spanish Generalist Translator Prompt Template

The 8-shot French generalist Translator prompt template is the same for all test ambiguity data and is provided in code
block listing 5.
def french_generalist_translator_interactive(en_text, question=None, answer=None):"""Translation model asks questions and uses answers to translate"""3
if answer == None:4# Ask questions
5
instructions = "[web] Given sentence ’S’ to translate to French, ask clarifying
questions ’Q’ to clarify ambiguities or multiple senses:"
6
else:
7
# Translate given answer
8
instructions = "[web] Given answer ’U’ to question ’Q’, provide the Frenchtranslation ’A’ of sentence ’S’. Provide the best answer:"
1
2

9

templated_input = """

10
11
12
13

S: about
Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?%s

14
15
16
17

S: rent
Q: Is "rent" a tenant’s regular payment for a property or to pay someone for the use ofsomething?%s

18
19
20
21

S: abstract
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?%s

22
23
24
25

S: You know where it begins, you never know where it ends...
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

26
27
28
29

S: This is for you, too.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

30
31
32
33

S: You know where it begins, you never know where it ends...
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

34
35
36
37

S: I’ll help you find it before [pr] does.
Q: What does "it" refer to?%s

38
39
40
41

S: [pr] must have forced it somehow.
Q: What does "it" refer to?%s

42
43
44

"""

45
46
47
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49
50
51
52

if answer is None:
templated_input = templated_input % (’’, ’’, ’’, ’’, ’’, ’’, ’’, ’’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ:"else:
templated_input = templated_input % (’\nU: "about" means approximately.\nA: environ, presque, quelque, a peu pres,approximativement’,’\nU: "rent" is to pay someone for the use of something.\nA: louer’,21Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction’\nU: "abstract" is an adjective that modifies "concept" in the phrase "abstract
concept".\nA: abstraction, abstrait’,’\nU: "you" is \’informal\’ since the speaker has familiarity with the listener
and uses the first name "Jerry".\nA: A qui as-tu parle ?’,’\nU: "you" is \’formal\’ since "you" refers to a customer or tourist that Freya
Carlson is greeting with the polite form "Mr.".\nA: Ceci est pour vous.’,’\nU: "you" is \’neutral\’ because it is a generic "you" that refers to people ingeneral going through a difficult moment.\nA: On sait ou cela commence, mais on ne
sait jamais ou cela se termine...’,’\nU: "it" is a key.\nA: Je vous aiderai a la trouver avant elle.’,’\nU: "it" is a gate.\nA: Il a du le forcer d\’une maniere ou d\’une autre.’,)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: {question}\nU: {answer}\nA: "return templated_input

53
54
55
56

57
58
59
60
61

Listing 5. I NTER CP T French Generalist Translator Prompt Template

D.3. I NTER CP T Specialist Prompt Templates for each target language
The Spanish formality specialist Translator prompt template is the same for all test ambiguity data and is provided in code
block listing 6.
def spanish_formality_translator_interactive(en_text, question=None, answer=None):"""Translation model asks questions and uses answers to translate"""3
if answer == None:4# Ask questions
5
instructions = "[web] Given sentence ’S’ to translate to Spanish, ask clarifying
questions ’Q’ to clarify ambiguities or multiple senses:"
6
else:
7
# Translate given answer
8
instructions = "[web] Given answer ’U’ to question ’Q’, provide the Spanishtranslation ’A’ of sentence ’S’. Provide the best answer:"
1
2

9

templated_input = """

10
11
12
13

S: This will accelerate your metabolic functions-- help you make the transition.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

14
15
16
17

S: Poor baby... here’s yours!
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

18
19
20
21

S: They could wait ’till you’re on the beach, then cut loose, or start firing right away.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

22
23
24
25

S: You think if I get contacts I’ll suddenly turn into the homecoming queen.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s26
27

S: For centuries, we have watched you, listened to your radio signals and learned yourspeech and your culture.
29 Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s
28

30
31
32
33

S: I never have. I’m not sure you’re supposed to.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

34
35
36

"""

37
38

if answer is None:

22

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
39
40
41
42
43

44

45

46

47

48

49
50
51

templated_input = templated_input % (’’, ’’, ’’, ’’, ’’, ’’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ:"else:
templated_input = templated_input % (’\nU: "you" is \’formal\’ since "you" refers to a Captain and the speaker will
typically use a polite form.\nA: Esto acelerara sus funciones metabolicas. Lo ayudara
a hacer la transicion.’,’\nU: "you" is \’informal\’ since the speaker has familiarity with the listener
and they both use "baby" and "buddy" to address each other.\nA: Pobre bebe... aquiesta el tuyo!’,’\nU: "you" is \’neutral\’ because it is a generic "you" that refers to people ingeneral and not someone specific.\nA: Podian aguardar a que uno estuviera en la playa
y atacar o comenzar a disparar.’,’\nU: "you" is \’informal\’ since the listener is the speaker\’s "mom", it implies
a familiarity with the listener "you".\nA: Tu piensas que si uso lentes de contacto
de repente me convertire en la nueva reina del colegio.’,’\nU: "you" is \’formal\’ since the speaker addresses people not acquainted withor unfamiliar.\nA: Durante siglos, los hemos observado, escuchado sus senales de radio. Hemos aprendido su idioma y cultura.’,’\nU: "you" is \’neutral\’ because it is a generic "you" that refers to people ingeneral that have been in this "line of work".\nA: Yo no. No creo que uno deba
acostumbrarse.’)
templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: {question}\nU: {answer}\nA: "return templated_input
Listing 6. I NTER CP T Spanish Formality Specialist Translator Prompt Template

The Spanish polysemy specialist Translator prompt template is the same for all test ambiguity data and is provided in code
block listing 7. Please note that the instructions for the translation step is different than the generalist or the formality
specialist template.
def spanish_polysemy_translator_interactive(en_text, question=None, answer=None):"""Translation model asks questions and uses answers to translate"""3
if answer == None:4# Ask questions
5
instructions = "[web] Given an English word ’S’ to translate to Spanish, toclarify ambiguities and understand multiple senses ask questions ’Q’:"
6
else:
7
# Translate given answer
8
instructions = "[web] Given answer ’U’ to question ’Q’, Translate word ’S’ into
Spanish and provide unique and non-repeating synonyms in ’A’:"
1
2

9
10

templated_input = """

11
12
13

S: abstract
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?%s

14
15
16
17

S: abstract
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?%s

18
19
20
21

S: about
Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?%s

22
23
24
25

S: bank
Q: Is "bank" to tilt sideways, or a financial institution, the edge of a river, a set orseries of similar things or the cushion of a pool?%s23

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
26
27
28
29

S: rent
Q: Is "rent" a tenant’s regular payment for a property or to pay someone for the use ofsomething?%s

30
31
32

"""

33
34
35
36
37
38
39
40
41
42
43
44
45
46

if answer is None:
templated_input = templated_input % (’’, ’’, ’’, ’’, ’’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: "else:
templated_input = templated_input % (’\nU: "abstract" is an adjective that modifies "concept" in the phrase "abstract
concept".\nA: abstraccion, abstracto’,’\nU: "abstract" means to extract something.\nA: abstraer’,’\nU: "about" means approximately.\nA: aproximadamente, cerca de, alrededor de,casi, mas o menos’,’\nU: "bank" is a financial institution.\nA: banco’,’\nU: "rent" is to pay someone for the use of something.\nA: alquilar, arrendar,
rentar’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: {question}\nU: {answer}\nA: "return templated_input
Listing 7. I NTER CP T Spanish Polysemy Specialist Translator Prompt Template

The French formality specialist Translator prompt template is the same for all test ambiguity data and is provided in code
block listing 8.
def french_formality_translator_interactive(en_text, question=None, answer=None):"""Translation model asks questions and uses answers to translate"""3
if answer == None:4# Ask questions
5
instructions = "[web] Given sentence ’S’ to translate to French, ask clarifying
questions ’Q’ to clarify ambiguities or multiple senses:"
6
else:
7
# Translate given answer
8
instructions = "[web] Given answer ’U’ to question ’Q’, provide the Frenchtranslation ’A’ of sentence ’S’. Provide the best answer:"
1
2

9
10

templated_input = """

11
12
13

S: This is for you, too.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

14
15
16
17

S: To whom have you been talking?
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

18
19
20
21

S: You know where it begins, you never know where it ends...
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

22
23
24
25

S: You can imagine the princess-sized tantrum that followed.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

26
27
28
29

S: City policemen questioned many of you this week.
Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s

30
31

24

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
S: You think you can make it through that kind of stuff, you think you can make it throughanything.
33 Q: "you" can be neutral, formal, informal. Who does "you" refer to?%s
32

34
35
36

"""

37
38
39
40
41
42
43
44
45
46
47
48
49
50
51

if answer is None:
templated_input = templated_input % (’’, ’’, ’’, ’’, ’’, ’’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ:"else:
templated_input = templated_input % (’\nU: \nA: Ceci est pour vous.’,’\nU: \nA: A qui as-tu parle ?’,’\nU: \nA: On sait ou cela commence, mais on ne sait jamais ou cela se termine...’,’\nU: \nA: Tu peux imaginer la colere de princesse qui a suivi.’,’\nU: \nA: Les gendarmes sont venus interroger nombre d\’entre vous.’,’\nU: \nA: On pense que quand on arrive a traverser ce genre de chose, on peut
traverser n\’importe quoi.’)
templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: {question}\nU: {answer}\nA: "return templated_input
Listing 8. I NTER CP T French Formality Specialist Translator Prompt Template

The French polysemy specialist Translator prompt template is the same for all test ambiguity data and is provided in code
block listing 9. Please note that the instructions for the translation step is different than the generalist or the formality
specialist template.
def french_polysemy_translator_interactive(en_text, question=None, answer=None):"""Translation model asks questions and uses answers to translate"""3
if answer == None:4# Ask questions
5
instructions = "[web] Given an English word ’S’ to translate to French, to clarify
ambiguities and understand multiple senses ask questions ’Q’:"
6
else:
7
# Translate given answer
8
instructions = "[web] Given answer ’U’ to question ’Q’, Translate word ’S’ into
French and provide unique and non-repeating synonyms in ’A’:"
1
2

9
10

templated_input = """

11
12
13

S: abstract
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?%s

14
15
16
17

S: abstract
Q: Is "abstract" to consider theoretically, to extract something, or a summary, or an
adjective?%s

18
19
20
21

S: about
Q: Is "about" an adverb that means approximately, near or a preposition that means
regarding, over, surrounding?%s

22
23
24
25

S: bank
Q: Is "bank" to tilt sideways, or a financial institution, the edge of a river, a set orseries of similar things or the cushion of a pool?%s

26
27

25

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
28
29

S: rent
Q: Is "rent" a tenant’s regular payment for a property or to pay someone for the use ofsomething?%s

30
31
32

"""

33
34
35
36
37
38
39
40
41
42
43
44
45
46

if answer is None:
templated_input = templated_input % (’’, ’’, ’’, ’’, ’’)templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: "else:
templated_input = templated_input % (’\nU: "abstract" is an adjective that modifies "concept" in the phrase "abstract
concept".\nA: abstraction, abstrait’,’\nU: "abstract" means to extract something.\nA: abstraire, extraire’,’\nU: "about" means approximately.\nA: environ, presque, quelque, a peu pres,approximativement’,’\nU: "bank" is a financial institution.\nA: banque’,’\nU: "rent" is to pay someone for the use of something.\nA: louer’)
templated_input = f"{instructions}\n" + templated_input + f"S: {en_text}\nQ: {question}\nU: {answer}\nA: "return templated_input
Listing 9. I NTER CP T French Polysemy Specialist Translator Prompt Template

D.4. LLM W C XT Generalist Prompt Templates for each target language
The 8-shot PaLM-with Context Spanish generalist prompt template is the same for all test ambiguity data and is provided incode block listing 10.
12

def spanish_baseline_generalist_translator_context(en_text, ctx):"""Translation model uses context to translate."""3
4

templated_input = f"""[web] Given context ’C’, Translate ’T’ from English to Spanish:

5

C: About 2% of the households are enumerated using the canvasser method.
T: about
8 A: aproximadamente, cerca de, alrededor de, casi, mas o menos
6
7
9
10

C: Many single women cannot live independently because they cannot (afford to) own or rent
housing
12 T: rent
13 A: alquilar, arrendar, rentar
11

14
15

C: For the international community is not an abstract concept, it consists of us ourselves.
17 T: abstract
18 A: abstraccion, abstracto
16

19
20

C: Daria, I just think that your field of vision could really be enhanced... - Come on,
Mom. - It’s not my field of vision you want to enhance. - What do you mean?
22 T: You think if I get contacts I’ll suddenly turn into the homecoming queen.
23 A: Tu piensas que si uso lentes de contacto de repente me convertire en la nueva reina delcolegio.
21

24
25

C: At the very least, get them to hold their fire. - Captain, the transporters are offline. - The docking port hasn’t been hit yet.
27 T: This will accelerate your metabolic functions-- help you make the transition.
28 A: Esto acelerara sus funciones metabolicas. Lo ayudara a hacer la transicion
26

26

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
29
30

C: Some of the guys got a little sick. - They were scared; I was scared. - I don’t think
we had any reason to be otherwise.
32 T: They could wait ’till you’re on the beach, then cut loose, or start firing right away.
33 A: Podian aguardar a que uno estuviera en la playa y atacar o comenzar a disparar.
31

34
35

C: Even when it is pouring outside, this umbrella is both practical and elegant.
T: It is also very pretty.
38 A: Es muy bonita tambien.
36
37
39
40

C: -Frog is wrong. - I see here that you play the harp. - Tell me, why do they have totilt it?
42 T: can’t they just build it on an angle?
43 A: no pueden hacerla en angulo?
41

44
45

C: {ctx}
T: {en_text}
48 A:"""
49
return templated_input
46
47

Listing 10. LLM W C XT Spanish Generalist Prompt Template

The 8-shot PaLM-with Context French generalist prompt template is the same for all test ambiguity data and is provided incode block listing 11.
1
2

def french_baseline_generalist_translator_context(en_text, ctx):"""Translation model uses context to translate."""3
4

templated_input = f"""[web] Given context ’C’, Translate ’T’ from English to French:

5

C: About 2% of the households are enumerated using the canvasser method.
T: about
8 A: environ, presque, quelque, a peu pres, approximativement
6
7
9
10

C: Many single women cannot live independently because they cannot (afford to) own or rent
housing
12 T: rent
13 A: louer
11

14
15

C: For the international community is not an abstract concept, it consists of us ourselves.
17 T: abstract
18 A: abstraction, abstrait
16

19
20

C: I believe! - -Who else knows? - -I don’t know. - Jerry, names! - I don’t want to dance!
T: To whom have you been talking?
23 A: A qui as-tu parle ?
21
22
24
25

C: I’m Freya. - Welcome to Denmark, Mr. Helm. - You always greet people like this? - I’m
Freya Carlson, your Tourist Bureau contact. - These are for you. Street maps, placesof interest.
27 T: This is for you, too.
28 A: Ceci est pour vous.
26

29
30
31

C: It’s like the city’s changed her. - Well, transitions are hard. - Been together eversince college. - Been through a lot. - You know, us coming out to her family, and her

27Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
brother dying.
T: You know where it begins, you never know where it ends...
33 A: On sait ou cela commence, mais on ne sait jamais ou cela se termine...
32
34
35

C: Even when it is pouring outside, this umbrella is both practical and elegant.
T: it is also very pretty.
38 A: il est aussi tres beau.
36
37
39
40

C: Okay, you don’t smash the cherry on that. Just plop it in at the end.
T: Try to keep it in the top of the glass.
43 A: Essaie de la garder dans le haut du verre.
41
42
44
45

C: {ctx}
T: {en_text}
48 A:"""
49
return templated_input
46
47

Listing 11. LLM W C XT French Generalist Prompt Template

D.5. LLM W C XT Specialist Prompt Templates for each target language
The PaLM-with Context Spanish Formality specialist prompt template is the same for all test ambiguity data and is provided
in code block listing 12.
12

def spanish_baseline_formality_translator_context(en_text, ctx):"""Translation model uses context to translate."""3
4

templated_input = f"""[web] Given context ’C’, Translate ’T’ from English to Spanish:

5

C: At the very least, get them to hold their fire. - Captain, the transporters are offline. - The docking port hasn’t been hit yet.
7 T: This will accelerate your metabolic functions-- help you make the transition.
8 A: Esto acelerara sus funciones metabolicas. Lo ayudara a hacer la transicion.
6

9

C: Who? - Me! - I think I’ve got a cold. - "Hey buddy, give me a Magic Hug will you!" Magic Hug! - And me? - Shut up Swami
11 T: Poor baby... here’s yours!
12 A: Pobre bebe... aqui esta el tuyo!
10

13

C: Some of the guys got a little sick. - They were scared; I was scared. - I don’t think
we had any reason to be otherwise.
15 T: They could wait ’till you’re on the beach, then cut loose, or start firing right away.
16 A: Podian aguardar a que uno estuviera en la playa y atacar o comenzar a disparar.
14

17

C: Daria, I just think that your field of vision could really be enhanced... - Come on,
Mom. - It’s not my field of vision you want to enhance. - What do you mean?
19 T: You think if I get contacts I’ll suddenly turn into the homecoming queen.
20 A: Tu piensas que si uso lentes de contacto de repente me convertire en la nueva reina delcolegio.
18

21

C: Men of earth, we of the planet Mars give you this warning. - We have known your planet
earth since the first creature crawled out of the primeval slime of your seas tobecome man.
23 T: For centuries, we have watched you, listened to your radio signals and learned yourspeech and your culture.
24 A: Durante siglos, los hemos observado, escuchado sus senales de radio. Hemos aprendido su
idioma y cultura.
22

25

C: Pull over here. This is the spot. - I guess you run into a lot of dead bodies in yourline of work. - You get used to it.
27 T: I never have. I’m not sure you’re supposed to.
26

28

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
28

A: Yo no. No creo que uno deba acostumbrarse.

29

C: {ctx}
T: {en_text}
32 A:"""
33
return templated_input
30
31

Listing 12. LLM W C XT Spanish Formality Specialist Prompt Template

The PaLM-with Context Spanish Polysemy specialist prompt template is the same for all test ambiguity data and is provided
in code block listing 13.
1
2

def spanish_baseline_polysemy_translator_context(en_text, ctx):"""Translation model uses context to translate."""3
4

templated_input = f"""[web] Given context ’C’, Translate ’T’ from English to Spanish:

5
6

C: Many single women cannot live independently because they cannot (afford to) own or rent
housing
8 T: rent
9 A: alquilar, arrendar, rentar
7

10
11

C: We need to abstract the data from various studies.
T: abstract
14 A: abstraer
12
13
15
16

C: About 2% of the households are enumerated using the canvasser method.
T: about
19 A: aproximadamente, cerca de, alrededor de, casi, mas o menos
17
18
20
21

C: The bat flew over the forest and back to its cave.
T: bat24 A: murcielago
22
23
25
26

C: For the international community is not an abstract concept, it consists of us ourselves.
28 T: abstract
29 A: abstraccion, abstracto
27

30
31

C: {ctx}
T: {en_text}
34 A:"""
35
return templated_input
32
33

Listing 13. LLM W C XT Spanish Polysemy Specialist Prompt Template

The PaLM-with Context French Formality specialist prompt template is the same for all test ambiguity data and is provided
in code block listing 14.
1
2

def french_baseline_formality_translator_context(en_text, ctx):"""Translation model uses context to translate."""3
4

templated_input = f"""[web] Given context ’C’, Translate ’T’ from English to French:

5

C: I’m Freya. - Welcome to Denmark, Mr. Helm. - You always greet people like this? - I’m
Freya Carlson, your Tourist Bureau contact. - These are for you. Street maps, placesof interest.
7 T: This is for you, too.
629

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
8

A: Ceci est pour vous.

9

C: I believe! - -Who else knows? - -I don’t know. - Jerry, names! - I don’t want to dance!
T: To whom have you been talking?
12 A: A qui as-tu parle ?
10
11
13

C: It’s like the city’s changed her. - Well, transitions are hard. - Been together eversince college. - Been through a lot. - You know, us coming out to her family, and herbrother dying.
15 T: You know where it begins, you never know where it ends...
16 A: On sait ou cela commence, mais on ne sait jamais ou cela se termine...
14

17

C: You know, if you’re gonna go for a spin, I suggest you get your helmet. - This is thebike that I learned to ride on. - I just didn’t know my mom kept it. - It used to havethese training wheels on the back with lights that would flash every time you pedaled. - Then one day, my mom took them off and said it was time to be a big girl.
19 T: You can imagine the princess-sized tantrum that followed.
20 A: Tu peux imaginer la colere de princesse qui a suivi.
18

21

C: He was in a state of shock, unable to walk. - Lying on his belly, he was carried homeon a makeshift stretcher. - Next Sunday, after the service, the Baron asked the pastor
to let him speak.
23 T: City policemen questioned many of you this week.
24 A: Les gendarmes sont venus interroger nombre d\’entre vous.
22

25

C: I tried to explain... He might have gotten hurt! - I was actually doing him a favour. Someone once told me we always are where we’re supposed to be. - Now I believe it. Life is a journey.
27 T: You think you can make it through that kind of stuff, you think you can make it throughanything.
28 A: On pense que quand on arrive a traverser ce genre de chose, on peut traverser n\’
importe quoi.
26

29

C: {ctx}
T: {en_text}
32 A:"""
33
return templated_input
30
31

Listing 14. LLM W C XT French Formality Specialist Prompt Template

The PaLM-with Context French Polysemy specialist prompt template is the same for all test ambiguity data and is provided
in code block listing 15.
1
2

def french_baseline_polysemy_translator_context(en_text, ctx):"""Translation model uses context to translate."""3
4

templated_input = f"""[web] Given context ’C’, Translate ’T’ from English to French:5

C: Consequently a strategy has been defined that allows departments to approach its
implementation in a step-wise manner.
7 T: approach
8 A: s’approcher, aborder, contacter, s’adresser
6

9

C: We need to abstract the data from various studies.
T: abstract
12 A: abstraire, extraire
10
11
13

C: About 2% of the households are enumerated using the canvasser method.
T: about
16 A: environ, presque, quelque, a peu pres, approximativement
14
15
17

C: The bat flew over the forest and back to its cave.
T: bat20 A: chauve-souris
18
19
21

30

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
C: For the international community is not an abstract concept, it consists of us ourselves.
23 T: abstract
24 A: abstraction, abstrait
22

25

C: {ctx}
T: {en_text}
28 A:"""
29
return templated_input
26
27

Listing 15. LLM W C XT French Polysemy Specialist Prompt Template

E. More details on gender and formality classifier
The classifiers fall into 2 categories: (1) heuristic based classification, that use the same language rules from section C.2;
(2) neural network based classification, using a PaLM 62B model with 8-shot in-demonstration exemplars. We provide
below the exemplars that were used to classify gender of French in code block listing 16 and Spanish sentences in code
block listing 17. Note that we added exemplars until we had a satisfactory score on our ground truth translated sentence (seeTable 7).
1
2

def french_gender_it_classifier_template(en_text, fr_text):"""Classify a French sentence as feminine or masculine. 7-shot examples"""3
4
5

templated_input =f"""[web] Given French sentence ’F’, provide the gender of "it" in English sentence ’T’
and explain in ’E’. Gender in ’A’ must be ’feminine’, ’masculine’ or ’neutral’:67

T: lily and marshall decided to sell it for one simple reason.
F: lyly et marshall l\’avaient mise en vente pour une seule raison.
10 A: feminine
11 E: It is ’feminine’ since "mise" refers to a feminine object.
8
9

12
13

T: - maybe you need to shake it up.
F: - peut-etre qu’il faut le secouer.
16 A: masculine
17 E: It is ’masculine’ since "le" refers to a masculine object.
14
15

18
19

T: i want you to get it for me.
F: Je veux que tu me la rapportes.
22 A: feminine
23 E: It is ’feminine’ since "la" refers to a feminine object.
20
21

24
25

T: put it back.
F: repose-le.
28 A: masculine
29 E: It is ’masculine’ since "le" refers to a masculine object.
26
27

30
31

T: I’m afraid i won’t be able to get it for you.
F: Je crains de ne pas pouvoir te l’obtenir.
34 A: neutral
35 E: It is ’neutral’ since we cannot determine gender with "l\’" only.
32
33

36
37

T: that view is even more beautiful when you have someone to share it with.
F: elle est encore plus belle si on n’est pas seul.
40 A: feminine
41 E: It is ’feminine’ since "it" refers to "view" in English and "vue" in French which isfeminine.
38
39

31

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
42
43

T: where’s it going?
F: ou va-t-il ?
46 A: masculine
47 E: It is ’masculine’ since "it" refers to "il" in French which is masculine.
44
45

48
49

T: {en_text}
F: {fr_text}
52 A: """
53
return templated_input
50
51

Listing 16. PaLM prompt template for gender classification of French sentences
1
2

def spanish_gender_it_classifier_template(en_text, es_text):"""Classify a Spanish sentence as feminine or masculine. 8-shot examples"""3
4

templated_input =5
6f"""[web] Given Spanish sentence ’F’, provide the gender in ’A’ and explain in ’E’. Gender’A’ must be either ’feminine’ or ’masculine’:7

F: nos habriamos pasado el dia mirandola.
A: feminine
10 E: It is ’feminine’ since "la" and verb "mirandola" refer to a feminine object.
8
9

11
12

F: - los peruanos no podian pronunciarlo.
A: masculine
15 E: It is ’masculine’ since "lo" in verb "pronunciarlo" refers to a masculine object.
13
14
16
17

F: Quiero decir, me encantaria volver a verlo.
A: masculine
20 E: It is ’masculine’ since "lo" in verb "verlo" refers to a masculine object.
18
19
21
22

F: debemos ponerla de vuelta?
A: feminine
25 E: It is ’feminine’ since "la" in verb "ponerla" refers to a feminine object.
23
24
26
27

F: -tiene que bebersela o tirarla.
A: feminine
30 E: It is ’feminine’ since "la" in verbs "bebersela" and "tirarla" refer to a feminine
object.
28
29

31
32

F: Guardalo para el proximo barco.
A: masculine
35 E: It is ’masculine’ since "lo" in verb "Guardalo" refers to a masculine object.
33
34
36
37

F: \"escuchandola me dan ganas de vivir.\"
A: feminine
40 E: It is ’feminine’ since "la" in verb "escuchandola" refers to a feminine object.
38
39
41
42

F: !cambialo al menos!
A: masculine
45 E: It is ’masculine’ since "lo" in verb "cambialo" refers to a masculine object.
43
44
46
47
48

F: {es_text.lower()}32

Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
49
50

A: """
return templated_input
Listing 17. PaLM prompt template for gender classification of Spanish sentences

We have added the classification heuristics and other classification templates to our public data and code repository.

Table 7. PaLM 62B gender classification results
on a 100 generated translation samples.

SpanishFrench97%93%33