# Tailoring Vaccine Messaging with Common-Ground Opinions

Rickard Stureborg Sanxing Chen Ruoyu Xie Aayushi Patel Christopher Li
Chloe Qinyu Zhu Tingnan Hu Jun Yang Bhuwan Dhingra
Duke University
{rickard.stureborg, sc829, rx55,
akp43, cl619, qz124<EMAIL_ADDRESS>
{junyang<EMAIL_ADDRESS>

###### Abstract

One way to personalize chatbot interactions is by establishing common ground
with the intended reader. A domain where establishing mutual understanding
could be particularly impactful is vaccine concerns and misinformation.
Vaccine interventions are forms of messaging which aim to answer concerns
expressed about vaccination. Tailoring responses in this domain is difficult,
since opinions often have seemingly little ideological overlap. We define the
task of tailoring vaccine interventions to a Common-Ground Opinion (CGO).
Tailoring responses to a CGO involves meaningfully improving the answer by
relating it to an opinion or belief the reader holds. In this paper we
introduce Tailor-CGO, a dataset for evaluating how well responses are tailored
to provided CGOs. We benchmark several major LLMs on this task; finding
GPT-4-Turbo performs significantly better than others. We also build automatic
evaluation metrics, including an efficient and accurate BERT model that
outperforms finetuned LLMs, investigate how to successfully tailor vaccine
messaging to CGOs, and provide actionable recommendations from this
investigation.111 Tailor-CGO dataset and code available at:
https://github.com/rickardstureborg/tailor-cgo

##  1 Introduction

Figure 1:  Example of a Tailored Response to answer a Vaccine Concern while
Tailoring to a Common-Ground Opinion. The response above is a shortened
version of a model response for the task of tailoring to CGOs. The responses
is able to relate two seemingly unrelated topics: side effects from
vaccination and a strong support for the military. It is strengthened by
language and analogies that may appeal to the user without becoming
manipulative. This work creates an evaluation framework and benchmarks
different LLMs on their ability to generate such tailored responses.

Finding common-ground has long been understood as an important aspect of
language and communication (Gibbs et al., 1988). Creating this connection
during communication can establish a sense of trust and is an inclusive goal
to strive for when preparing personalized messaging. We can establish common
ground on many things, such as beliefs or opinions, although it may be unclear
whether such opinions can be seamlessly related to the main message being
communicated.

One particularly relevant use case for establishing common ground is vaccine
messaging. Loss of trust and polarization are increasingly pervasive issues
impacting vaccine hesitancy (Ozawa and Stack, 2013). Those involved with
vaccine messaging (e.g. vaccine navigators, public health organizations,
healthcare professionals) could be aided by further tools to help write
responses, personalize information, or generate creative ideas for their
messaging efforts.

Large Language Models (LLMs) have demonstrated high performance in controlled
text generation. Recent research has increasingly explored personalization
given the abilities of these models (Soni et al., 2022; Welch et al., 2022;
Salemi et al., 2023) with many pointing out remaining challenges (Kirk et al.,
2023). How LLMs handle personalization in terms of opinions and beliefs
remains under-explored.

To this end, we propose a task for tailoring vaccine messaging towards common-
ground opinions (Figure 1). By providing common-ground opinions and
instructing LLMs to tailor towards them, we hope to help address the imbalance
and inequity in the broader information landscape surrounding vaccination.
Given that some work (Santurkar et al., 2023) has pointed out these models may
exhibit biased opinions that do not reflect diverse or underrepresented
groups, we highlight and evaluate current major LLMs’ ability to perform this
task by building comprehensive automatic evaluation metrics.

This paper provides the following contributions:

  * •

Evaluation of several major LLMs on their ability to use ‘Common-Ground
Opinions’ in vaccine messaging.

  * •

Tailor-CGO, a comprehensive and high-quality dataset for training and
evaluation.

  * •

Actionable recommendations of which CGOs are most useful to address a given
concern.

##  2 Related Work

Personalization has been explored in various NLP tasks, most notably within
the domain of dialogue response generation (Wang et al., 2019; Zhang et al.,
2018; Zheng et al., 2019; Joshi et al., 2017). Using natural language prompts,
language models can generate texts that align with demographics or identities,
reflecting cross-cultural values (Arora et al., 2023), political ideology
(Simmons, 2023), or opinions on societal issues (Argyle et al., 2023;
Santurkar et al., 2023), or infer personal attributes (Wang et al., 2022).
Previous work has also investigated balancing benefits and harms of
personalization (Kirk et al., 2023), providing benchmarks (Salemi et al.,
2023), and constructing user-conditioned language models (Soni et al., 2022;
Welch et al., 2022). However, we are unaware of any work to date which
examines personalization of LLM responses within the vaccine misinformation
domain, or work which focuses on tailoring messages to common-ground opinions.
There is also a line of work in NLP on grounding responses (Cho and May, 2020;
Chandu et al., 2021; Zhou et al., 2022).

Various benchmarks have been proposed to test general LLM abilities in
generating task-specific responses (Hendrycks et al., 2021; Khashabi et al.,
2022; Zheng et al., 2023). Our benchmark emphasizes on a coverage of diverse
opinions for controllable generation in the domain of vaccine concern and
misinformation. In the era of LLMs, we have seen a renewed interest in
automatic evaluation metrics of text generation, due to the need for
reinforcement signals (Stiennon et al., 2020b; Rafailov et al., 2023). We
demonstrate the feasibility to build automatic evaluation metrics using our
data, facilitating future efforts to improve LLM generation.

Previous research of misinformation has explored classification of common
concerns and misinformation topics (Coan et al., 2021; Stureborg et al.,
2024b; Zhu et al., 2024), fact-checking statements (Thorne et al., 2018), or
claim review (Arslan et al., 2020b, a), which determines if claims are worth
fact-checking. While addressing these concerns and misinformation is
important, our work aims to begin addressing the vaccine misinformation
through tailored messaging.

Indeed, there is already substantial work on establishing common ground for
the goal of successful communication. It is well understood in cognitive
sciences (Clark and Carlson, 1982; Clark and Schaefer, 1989; Clark and
Brennan, 1991). Likewise, arguments tailored to information about the target
audience have been shown more effective (Hirsh et al., 2012; Hadoux and
Hunter, 2019). However, reliably generating such responses automatically is an
open research question which could serve as the foundation for future research
into effective communication practices or persuasion.

##  3 Tailor-CGO Dataset Creation

In this section we describe the task of tailoring a response to a CGO,
introduce and describe the dataset and its components, and outline how the
dataset is labeled. The final dataset contains $22,400$ unique tailored
responses from 6 different LLMs, labeled with a mix of absolute scores or
pairwise rankings.

###  3.1 Task Definition

We define our task of tailoring to common-ground opinions as follows. In
response to an expressed _concern_ about vaccination, the task is to generate
an intervention tailored to a given _common-ground opinion (CGO)_ , which
should act as the basis for framing the response.

A successfully tailored response should meet five criteria: (1) It should
fully answer the concern to promote vaccination or encourage engaging further
with health professionals. (2) The opinion should be used or referred to in
the response, either directly or indirectly. (3) The response should accept
the opinion as true, rather than refute it. (4) The answer to the concern
should be meaningfully linked to the opinion in some manner. Finally, (5) the
use of the opinion should strengthen the response to the expressed concern,
such that the removal of the opinion would weaken the response.

###  3.2 Concerns and Opinions Statements

To provide inputs for our task, we need explicitly stated concerns and
opinions. For concerns, we utilize the VaxConcerns taxonomy from Stureborg et
al. (2024b) and then prompt GPT-4 to generate a large variety of concern
statements, as detailed in Appendix I. Some of these statements refer to
specific vaccines (COVID-19, HPV, MMR, Influenza, and Yellow Fever), while
others are agnostic towards vaccine type. There are 1166 total concern
statements, all cleaned by the authors of this paper and mapped to the
VaxConcerns taxonomy. One example concern is shown below, where the sampled
vaccine-type was ‘MMR’ and the concern category was ‘2.4: Lack of benefits
$\to$ Insufficient risk’.

> “Measles, mumps, and rubella cases are so rare nowadays, the MMR vaccine
> seems unnecessary.”

For opinions, we generate statements by paraphrasing questions from OpinionQA
(Santurkar et al., 2023). These public opinion survey questions (originally
sourced from PEW Research polls) have awkward phrasing which make them
difficult to use as-is. Therefore, we use GPT-4 to convert these questions to
single-sentence statements expressing the opinion as a fact. For example, one
such opinion statement reads:

> “In general, society tends to look up to men who are manly or masculine
> these days.”

###  3.3 Response Generation

To produce candidate responses for annotation, we prompt LLMs to tailor to
common-ground opinions. However, we do not want downstream uses of our dataset
to rely heavily on a specific combination of model and prompt. Therefore, we
use a variety of systems for producing candidate responses. We conducted an
extensive qualitative analysis of the generated responses to find common
issues and strengths, which are detailed in Appendix F.

We generate a mix of candidate responses that allow for both intra-opinion
comparisons, where responses are tailored to the same opinion, and inter-
opinion comparisons, where responses are tailored to different opinions.
Specifically, we create “blocks” of 4 response generations, with each block
structured as follows:

⬇

Concern1 + Opinion1 -> ResponseA (rA)

Concern1 + Opinion1 -> ResponseB (rB)

Concern2 + Opinion2 -> ResponseC (rC)

Concern2 + Opinion3 -> ResponseD (rD)

Here, $(rA,rB)$ provides an intra-opinion comparison and $(rC,rD)$ provides an
inter-opinion comparison. For each block, we randomly sample 2 concerns and 3
opinions with replacement. For response generation, we randomly sample model,
prompt, and temperature from their respective domains as explained in the
following subsections. For half of our blocks, we fix these system parameters
(model, prompt, temperature) between $rA,rB$ and between $rC,rD$. This design
gives opportunities to compare responses sampled under identical settings as
well as different ones.

We manually inspected 700 sample responses of the 1546 unguided responses.
Within this sample, we found 21 instances of causes of clear response failure,
demonstrating a 3% failure rate. There were several distinct failure modes
that we identified with varying frequencies: (a) 1.9% - The model directly or
indirectly assumes the identity of a human 13 times, (b) 0.3% - The model
assumes an identity for the person it is responding to 2 times, (d) 0.3% - The
model responds to a different vaccine concern than that which is provided in
the prompt 2 times, (f) 0.3% - The model crafts a response that contains a
template element like “hey [friend’s name]” 2 times (c) 0.1% - The model
explicitly disagrees with the CGO 1 time, and (e) 0.1% - The model makes a
factually incorrect or ambiguous statement 1 time.

#### Models

We consider six models: Llama-2, Vicuna, WizardLM, GPT-3.5, GPT-4 and
GPT-4-Turbo. In early experiments, such as collection of the dev set, we use
smaller model sizes ($13$b models) for candidate response generation. However,
in the final round-2 and round-3 training and test data, we use the most
powerful models possible on our hardware (70B parameter models). A full list
of the model checkpoints we use, with complete citations and links, are shown
in Table 1. We randomly sample temperature uniformly between 0 and 1 during
generation to encourage a diversity of both creative and high-likelihood
outputs.

Table 1: Models checkpoints used in this work.  Model Checkpoint
---
llama-2-13b-chat-hf
llama-2-70b-chat-hf
vicuna-13b-v1.5
vicuna-33b-v1.3
WizardLM-13B-V1.2
WizardLM-70B-V1.0
gpt-4-1106-preview
gpt-4-0613
gpt-3.5-turbo-0613

#### Prompting

To increase the diversity of our prompts, we make use of role-playing (Wang et
al., 2023), chain-of-thought (Wei et al., 2022), and pointed instructions for
generation (Ouyang et al., 2022). We consider all combinations of the
following strategies:

  * •

Roles: We ask the model to produce different text styles by instructing it
with a role (e.g. parent, doctor, redditor) to take when responding. We use 10
different roles.

  * •

Chain of Thought (CoT): The model is asked to first think through the process
of generating the tailored response by writing out a plan, and is then
subsequently prompted to produce the final response. We compare the quality of
responses with and without CoT.

  * •

Guidelines: The prompt includes written instructions for principles to follow
or general behaviors to avoid. These are sourced from 1) CDC guidelines on
vaccine messaging, 2) the criteria (§3.1) for what makes well-tailored
responses, and 3) general guidelines to avoid issues noticed in responses
during the development phase. To avoid increasing the size of the prompt
prohibitively, and to ensure diversity, we randomly sample five such
guidelines from all sources uniformly and include them in prompts marked with
the ‘guided’ flag. Approximately half of responses are produced with a guided
prompt.

Overall, these strategies are each randomly sampled, leading to 40 potential
prompts types, with more than 500K possible unique combinations (due to the
random sampling of guidelines).222 40 prompt types come from the 10 roles, 2
settings for CoT (on or off), and 2 settings for guidelines (on or off).
Accounting for all possible sampled subsets of 5 from the 22 guidelines gives
us 500K unique combinations ($20+20\cdot_{22}C_{5}$)

###  3.4 Human Annotation

We collect data in 3 rounds. First, we annotate tailored responses using both
absolute scoring and relative preferences in parallel randomly assigned
conditions. This is used as our dev set (Yellow). We find that relative
preferences yields higher data annotation quality, so in a second round we
invite back the 8% most accurate crowdsource workers to label our test (Blue)
and training (Green) sets. Third, we label a much larger set of tailored
responses using LLM evaluators. Figure 2 shows a useful diagram explaining the
various partitions of the final dataset. We reference these partitions
frequently in this section.

Figure 2:  Tailor-CGO dataset partition sizes. Colors indicate which
train/dev/test split each partition is included in. Green = train, Yellow =
dev, Blue = test. Relative preferences are collected by asking which of two
responses is better tailored, while absolute scoring asks for a 1-5 score for
a single response. Both Dev and Test sets (Yellow and Blue) contain 3
independently collected annotations per input response, represented by 3
stacked boxes. The training set (Green) contains just one annotation per
response to maximize diversity.

#### Anotation Scheme

To score the quality of candidate tailored responses, we collect preferences
using crowdsource annotations through Amazon Mechanical Turk (AMT). Given a
particular concern-opinion and response pair, annotators are asked to judge
the quality of the response based on the criteria listed under Task
Definition. We consider two potential annotation schemes:

  * •

Absolute scoring: Annotators are asked to make absolute judgments of how well-
tailored each response is on an individual basis. These judgments are given on
a 1-5 scale, ranging from Very poorly tailored to Very well tailored. However,
when performing within-team annotations, we found that Likert-style scales
were difficult to use since it is hard to calibrate what level of quality
warrants being well tailored versus very well tailored.

  * •

Relative preference: To circumvent the difficulty of absolution scoring, we
instead ask annotators to make relative judgments, comparing two responses
against one another and ranking them according to how well-tailored they are.
This label set is ordinal, with Response A (is better), Equal, and Response B
(is better), in order.

Our round-1 results indicated that collecting relative preferences leads to
higher agreement about the eventual ranking of responses. Annotators labeling
preference of two responses directly agree on 57.3% of labels, while marking
an absolute score for a single response yielded agreement of just 44.2%. One
reason for this is an increase in ties: absoluting scoring yield $19.5\%$ tied
preferences between pairs, while directly asking for preference in the pair
results in $8.5\%$ ties. This increase in ties is problematic not just for
annotator agreement, but potentially also for labeling efficiency, since we
lose information about nuanced differences in pairs with tied absolute scores.

#### Annotator Selection

We train our crowdsource workers using a short 9-example tutorial, where they
first annotate and are then given feedback with reasons motivating the correct
choice as well as highlights over the responses with hover-text providing
further explanations. We open our annotation task to all annotators which have
an AMT ‘masters’ qualification and at least 2,500 approved HITs at an approval
rate of at least 99%. These very selective criteria ensure only the highest
performing annotators. After the tutorial, annotators complete a short
(3-example) entrance exam of easier, expert-labeled questions to further
qualify; we remove any worker who incorrectly answers at least one of the
questions. During annotation, we randomly insert, for 5% of shown examples,
attention checks that instruct workers to select certain options to ensure
they are fully reading the passages.

We invite only the top 8% of annotators for round-2 annotations based on their
scores on the tutorial examples. We pay approximately 15-20 USD per hour for
the workers in our round-1 annotation, and 25-30 USD per hour for those in
round-2 annotations. We offer $100 bonuses to the top 25% of annotators in
round 2 to incentivize high-quality annotations, and regularly examine their
annotations and offer feedback through direct messaging during data
collection.

Further details of the annotation platform, including screenshots, are
available in Appendix C

#### Inter-annotator Agreement

Round-2 annotations (test set marked in blue and human-based training set
marked in green in Figure 2) show much higher agreement than round 1. As shown
in Table 2, crowdsource annotators agree on 73.2% of labels, comparable to
previous works (Stiennon et al., 2020a; Ouyang et al., 2022) on annotating
human preferences for reward modeling.

Annotation Strategy | Percent agreement
---|---
Dev set (round 1 - absolute) | 44.2%
Dev set (round 1 - relative) | 57.3%
Test set (round 2 - relative) | 73.2%
Bai et al. (2022) | 63.0%
Stiennon et al. (2020a) | 73.0%
Ouyang et al. (2022) |  77.3%
Table 2: Agreement between crowdsource annotators when presented with two
tailored responses. Our test set shows comparable amounts of agreement to work
in instruction tuning, despite a highly subjective and difficult task and
offering 3 options (A, Equal, B) rather than two (A, B) in the annotation
interface. Note that the human-labeled training set was collected together
with the test set, but does not have multiple annotations and is therefore
left out of this table.

##  4 Automatic Evaluation

While human annotation shows a high agreement on evaluating LLM responses,
conducting it at scale to study the various settings we consider is
prohibitively expensive. Hence, in this section we use the annotations
collected above to develop automatic evaluation metrics for the Tailor-CGO
task. The goal of automatic evaluation is to provide a cheap alternative for
labeling candidate responses and automatically evaluate or compare models. It
also allows for deeper analysis of trends when human annotation becomes
restricted by scale. We describe our approach below. First, we prompt a
generative model (GPT-4-Turbo) to score responses directly. Second, to further
reduce cost, we fine-tune open-source language models (BERT and Llama-2) using
the results from the former and/or human annotations.

###  4.1 Zero-shot Prompting

We use GPT-4-Turbo as an automatic evaluator by prompting it in a zero-shot
setting. Specifically, we use G-Eval’s (Liu et al., 2023) instruction template
by replacing the definition and evaluation steps in its provided prompts with
descriptions explaining what a well-tailored response is. G-Eval is an
automatic evaluation framework for text summarization built on top of GPT
models, but has been adopted to other tasks as well.

Because log probabilities are no longer available for GPT models, we sample
100 predictions at temperature 1.0 for each response, stopping the model
outputs at 10 tokens. We then parse these outputs to collect discrete 1-5
scores, and a mean score is calculated over the 100 samples thereby
approximating the original weighted prediction by token probabilities used in
G-Eval. We provide all outputs (raw and cleaned) in our dataset. When needed,
these absolute scores can be paired together with another response answering
the same concern, and by comparing the scores a relative preference can be
reported. This is how we perform evaluation on the Test set, and also how we
provide large labeled datasets to our finetuning methods.

###  4.2 Fine-tuning

We further explore the possibility of using open-source language models to
perform automatic evaluation, thus reducing the cost on closed-source API
inference. We test both an encoder-only model (BERT-base) and an auto-
regressive model (Llama-2-13b) in a knowledge distillation setup, where they
are trained on a large dataset of responses scored by GPT-4-Turbo. We continue
finetuning beyond the Round 3 dataset by using the human-labeled data from the
Dev set. In the relative preference setting, this is done by either randomly
mixing in the human labeled data, or training on this data after the model
converges. In the absolute scoring setting, we use a margin ranking loss.

#### BERT Fine-tuning

We fine-tune a 110M BERT model Devlin et al. (2019). For absolute scoring, we
train for regression on a 1-5 score. For relative comparison, we train as a
binary classification task (as opposed to using a parallel contrastive loss
sometimes used in such similar settings (Seth et al., 2023)). The model takes
as inputs the text of a sampled response pair from the round-3 training data.
On top of this model, we add a linear layer that maps the final BERT layer
hidden states into one (absolute scoring) or two scalars (relative
preferences). Mean-squared error and cross-entropy loss are applied
correspondingly. We sample approximately 10,000 responses (pairs) along with
their scores from GPT-4-Turbo to construct the training data. We train using a
batch size of 8 for 5 epochs, and the AdamW Loshchilov and Hutter (2019)
optimizer with a learning rate of 2e-5, 10% linear warm-up, and linear decay
to 0. Training and evaluation is done on a single NVIDIA A5000 GPU.

#### Llama-2 Fine-tuning

For both absolute scoring and relative preference settings, we use a 13B
LLaMA-2 as the base model for fine-tuning. We utilize QLoRA (Dettmers et al.,
2023) for computational efficiency and use the AdamW optimizer to train the
model for 5 epochs with a batch size of 4. The training is conducted on 4
NVIDIA RTX A6000 GPUs, setting the learning rate to 2e-4 with a 3% warmup. The
LoRA rank and alpha are set to 64 and 16, respectively, with a 0.1 dropout
between the two matrices.

We use Alpaca’s training prompt format (Bommasani et al., 2021) where the
instruction is replaced by the evaluation instruction and metrics, the input
by the concerns and opinions, and the response by either the tailoring score
or preferred response, depending on the setting. For the evaluation prompt, we
use zero-shot prompting in both settings. The temperature is set to 1 for the
fine-tuned LLaMA model. To extract the model’s answer, we only use the first
sentence of the response. For the absolute scoring setting, we search for
numeric values in the sentences. For the relative preference setting, we look
for either A or B in the sentence.

###  4.3 Performance

Table 3 summarizes each automatic evaluator’s performance on Tailor-CGO. The
fine-tuned BERT model outperforms all other models. meaning the student model
generalized better in this case. This result mirrors similar observations made
about self-distillation, which has been shown to have a regularizing effect
Furlanello et al. (2018); Mobahi et al. (2020). Absolute scoring performed
better in both GPT-4-Turbo and BERT, while relative preferences were more
accurate in Llama-2. We also found that continuing finetuning with the human-
labeled data after first training on LLM labeled data improved performance for
all our models. In the relative preference setting, this alone improved
Llama-2 performance from $69.1\%$ to $73.9\%$. We observed that Llama-2
finetuning is sensitive to hyperparameters, but due to limited time and
resources we were not able to fully tune these to improve beyond the
performance of the BERT models.

Incorrect predictions of Equal should not be penalized the same as mismatches
between Response A and Response B, so we compute a “lenient” version of
accuracy: we ignore these errors by removing all Equal predictions before
computing accuracy (treating them as Abstains).

Model | Setting | Dev | Test
---|---|---|---
GPT-4-Turbo | Pref. | 58.5 | 69.3
Score | 65.5 | 76.5
Llama-2 (13B) | Pref. | 69.8 | 73.9
Score | 62.7 | 68.7
BERT | Pref. | 62.0 | 77.0
Score | 65.0 | 80.8
Table 3: Accuracy (%) of Automatic Evaluators on Dev and Test Sets. Evaluators
are built in two settings: predicting relative preferences (Pref.) between two
input responses, or predicting absolute scores (Score) for a single input
response. Accuracy is then computed on Dev and Test sets as the percentage of
pairwise preferences the model correctly ranks.

Training on relative preferences sometimes performs worse than training with
absolute scores, despite the higher quality data in the human-labeled
partition (§3.4). This could potentially be attributed to a loss of
information: for both the case where rA was _much_ better than rB and the case
where it was only slightly better, the eventual label in the relative
preference setting is the same, while absolute scoring distinguishes these.
Therefore, there is a potential tradeoff between annotation quality (best
annotation type for crowdsource workers) and training efficiency (best
annotation type for models) that could be studied further. On the other hand,
forcing a decision when two responses are indistinguishable may be a new
source of noise.

##  5 Results

To better explore the usefulness of Tailor-CGO towards improving our
understanding of tailored responses, we investigate what models, strategies,
and opinions work best. This section describes a series of analyses looking at
factors to consider when generating responses tailored to CGOs, which may be
of interest to NLP researchers as well as public health professionals working
on vaccine hesitancy.

Figure 3:  Heatmap of mean scores by LLM evaluation for responses answering a
concern (horizontal axis) while tailoring to a CGO (vertical axis). Brighter
colors indicate higher scores, while white squares are nulls that were not
sampled during annotation. Religion, while an opinion topic that scores poorly
in our testing, seems to provide useful opinions for tailoring when focusing
on the Direct transmission concern (see Section F.3 for an example output).

###  5.1 Opinion Selection

A driving motivation for this work is to allow analysis as to which opinions
are fruitful for tailoring vaccine messaging on. To this end, we conduct an
analysis of which opinions led to the best tailored responses by examining the
mean scores (Figure 4) within topic clusters of opinions. We use the 24 topics
proposed and annotated by Santurkar et al. (2023) for the Pew Research’s
American Trends Panels questions.

For the analysis in Figure 4, we collect all candidate tailored responses in
the round-3 data if they tailor to an opinion associated with our topic of
interest. The GPT-4-Turbo assigned score for each such responses is then
computed by drawing 100 predictions and averaging; the final reported score is
a second average taken over each candidate response’s score. To determine a
95% (bootstrap) confidence interval, we repeat this process 10,000 times for
each topic by drawing from the candidate responses with replacement. The
analysis is repeated for each of the 24 topics.

Figure 4:  Comparison of mean response quality for each CGO, aggregated by
topic. Notice that potentially controversial and problematic topics such as
discrimination, race, or religion are bad targets for tailoring. The
implications of this result is that using divisive topics to establish common-
ground may be less useful, and using less polarized topics (self-perception)
for example can result in stronger overall scores.

However, some opinions may be better suited for use in a small subset of
concerns. We therefore investigate the response quality when the CGO belongs
to each topic by repeating the process on each concern category in the
VaxConcerns taxonomy (Stureborg et al., 2024b). The mapping between concerns
and these concern categories is discussed further in §3.2.

Figure 3 shows a visualized heatmap of the mean scores of each <opinion-topic,
concerns-category> combination. These results indicate that topics can indeed
be better suited for tailoring responses to some concerns than others. For
example, job/career opinions do very well on average with the Insufficient
risk and Direct transmission concerns (approx. 4/5), but quite poorly with
concerns regarding Existing alternatives (approx. 2/5). To improve automatic
vaccine messaging, dynamically selecting the right opinions when addressing a
concern could be a key strategy to improving response quality from models. We
note that broad concerns (Level 1 in the VaxConcerns taxonomy) are generally
easier to address than specific concerns (Level 2). This may be because
specific concerns are harder to creatively link with a given CGO, while
broader concerns offer more potential ways to relate the two topics.

Figure 5:  Comparison of Mean Response Quality by each Model in the LLM-
annotated train set partition. All differences in the figure are statistically
significant. Confidence intervals are computed through bootstrap sampling.
Each model is evaluated across approximately 4,000 generated responses each to
randomly sampled concern and opinion statements. We see GPT-4-Turbo produces
the best tailored responses on average, just ahead of GPT-4. Open-source
models still lag far behind, despite using the largest possible model sizes on
our hardware.

###  5.2 Model

We benchmark the performance of several major LLMs through the large-scale
data collection in Tailor-CGO. Figure 12 shows a breakdown of model
performances as determined by human annotators. While these results are
helpful, further analysis requires larger datasets, for which we use the LLM
based annotations of the round 3 partition (Figure 2) as shown in Figure 5.
GPT-4-Turbo is shown to perform the best on Tailor-CGO, and is subsequently
used for an analysis as to best prompting strategies in §5.3. Additionally,
Figure 6 shows a closer look of exactly where GPT-4-Turbo performs better than
the next best model, GPT-4 by examining the distribution of scores assigned to
tailored responses written by each model.

Figure 6:  Score distributions for GPT-4 and GPT-4 Turbo in round-3 dataset
partition. GPT-4-Turbo has a slightly better mean score than GPT-4 (3.58 vs
3.51). The distributions of response quality is generally comparable between
the models, with GPT-4-Turbo producing notably higher proportion of scores
around 4. Note that the distribution of scores seems to be bimodal,
potentially due to scores either successfully finding or not finding a useful
linking idea between the concern and CGO.

###  5.3 Prompting

We determine the best prompting method using the best model outlined in §5.2.
We compare configurations of the 3 prompt dimensions described in §3.3. Roles
are compared against each other in Figure 13, and the score distribution for
two selected roles is plotted in Figure 14, both in Appendix G. Figure 15 in
Appendix H describes the difference between Chain-of-Thought (CoT) prompting
and standard prompting (non-CoT). Standard prompting is significantly better
than CoT. For GPT-4-Turbo, the best prompting strategy is to use the Health
Expert role with guidelines and non-CoT prompting.

There is not a statistically significant improvement in model responses
through use of the guidelines, although our qualitative observations had
indicated that guidelines led to responses that better adhered to the
principles included by the guidelines. However, we investigate the case where
guidelines are sampled or are left out entirely, and there could be more work
to determine if a subset of the guidelines significantly improves the response
quality.

###  5.4 Expert Evaluation

We asked 3 senior public health experts to rate and comment on the quality of
the generated responses to validate their potential usefulness to a vaccine
navigator. They collectively rated 60 unique pairs of tailored responses, and
were asked to select their preference and give comments describing their
general impressions for who these responses are appropriate to. All responses
were generated from the strongest model in Section 5.2, but in each pair one
was a randomly chosen response, while the other was filtered as the best of 20
random generations by the BERT-based automatic evaluator from Section 4.

We were not able to find a statistically significant difference between the
filtered response and the randomly chosen ones in this sample. This may be due
to the model’s overall strength, as evidenced by the claimed usefulness of the
responses according to the experts. Experts overall described the responses as
“very high quality” and noted these responses could “easily be used by vaccine
navigators”. Responses were sorted into four categories for overall quality:
Low, Medium-Low, Medium-High, and High. Only $5\%$ of responses were
categorized as “Low” quality, $20\%$ were “Medium-Low” $20\%$ were “Medium-
High”, and $55\%$ were “High”.

Further, for $87.5\%$ of responses, they said they would be useful to a
vaccine navigator. For $55\%$ of responses, they said they would even be
comfortable with a patient reading the response. The main issues identified in
responses were to do with being too technical, most often meaning they did not
recommend showing these to patients. This seems to indicate that the responses
by the strongest identified model in this work may be good enough to aid a
vaccine navigator in their work, demonstrating the potential direct
application of the framework.

##  6 Conclusion

We introduce Tailor-CGO, a comprehensive and high-quality dataset for training
and evaluation of tailoring vaccine interventions to common-ground opinions.
We benchmark several major LLMs, finding that GPT-4-Turbo best tailors
responses to CGOs. We build evaluation metrics on top of this dataset to allow
cheap and accurate evaluation of models. Finally, we analyze which opinions
are better suited to tailor vaccine interventions with, and provide
recommendations for which opinions to select for specific concern categories.

## Limitations

Finding opinions for tailoring. In this work we present methods to tailor on
common-ground opinions. We assume that we are given these opinions and the
intended audience believes or strongly believes in them. However, identifying
beliefs is a difficult task on its own and requires further research. The
easiest way to confirm the audience holds the opinion is to survey them, but
doing so may affect further communication in other ways.

Crowdsource Workers’ Biases. We attempt to define annotation tasks that should
be “objective” regardless of who is labeling (thereby our focus on inter-
annotator agreement). However, each annotator brings in their own personal
biases. Opinions that seem questionable or off-putting to one annotator may
influence their ranking on that example.

Tailoring versus Engagement and Subjectivity. One motivation for this work is
to provide a framework for generating candidate responses in vaccine
interventions. To create a well-defined task, we measure how well concepts are
related (linked) in writing. What we do not directly measure is how engaging,
persuasive, or applicable each response is to the actual person reading it.
Such tasks are much more subjective, and require extensive invitation of
participants from diverse backgrounds in order to ensure solid research
findings. Individual identities are highly inter-sectional, and it can be hard
to recruit participants for whom vaccine messaging is intended for.

Diversity of Generated Responses. We attempt to create a large diversity in
responses by sampling many different models, under varied instructions, and
with a variety of concerns and opinions. However, we are still restricted in
the diversity we are able to generate on many dimensions. We begin to explore
this further in B. Future work could explore tasks such as tailoring longer
documents, or technical writing.

Use of Automatic Evaluation. It has been pointed out that LLM evaluators have
remaining challenges such as poor performance on higher-quality models (Shen
et al., 2023) or a preference for text generated by itself, likely due to a
bias in low-perplexity examples (Stureborg et al., 2023). Our work therefore
uses other methods of evaluation as well, such as direct accuracy comparisons
with human labeled data, and averages over several evaluations as suggested in
Stureborg et al. (2024a).

## Ethics statement

By the nature of opinions and vaccine misinformation, the research in this
paper comes with important ethical considerations. Some opinions from the PEW
Research surveys are bound to be controversial and asking generative models to
assume such opinions as truth may produce potentially harmful responses. Thus,
to use tailored responses, there is a great deal of responsibility in
selecting common-ground opinions to tailor on to avoid perpetuating harm. Some
of the CDC-sourced principles for vaccine messaging address these issues, such
as the principle that “CDC’s communication is empathetic, respectful, non-
judgmental, and never arrogant toward others”. The systems which our work
begins to explore are not yet performing at a high level of tailoring. Many
responses are completely artificial and don’t seem very persuasive. However,
our results indicate that producing well-tailored responses is possible. Given
the potential uses (and misuses) of such systems which this work begins to
explore, it is paramount that more research is done on safety and harm within
this field.

## Acknowledgements

We are very grateful to Walter Orenstein, Robert A. Bednarczyk, and Lavanya
Vasudevan for their help with the evaluation of response quality. A special
thanks to Christopher Kjellqvist for many useful discussions on signals for
personalization and persuasion, and the whole NLP group at Duke for their
helpful feedback and input on the project. Thank you to the anonymous
reviewers for their feedback.

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##  Appendix A Example Responses

Figure 7:  Example of Strong vs Weak Response The strong response leverages a
powerful analogy using the CGO to address the concern, making the crucial
point that despite the common end result of vaccination and natural immunity,
vaccination makes the process easier and much safer. The weak response is
generic, failing to both address the specific concern regarding potentially
harmful chemicals as well as using the CGO in its argument in any meaningful
way.

##  Appendix B Response Lengths in Tailor-CGO

Figure 8:  Distribution of length of responses in Tailor-CGO. Our responses
cover a range of short to medium length passages. All generative models were
instructed to answer the concern in “a short paragraph”. Responses are
generally within the token limits of models such as BERT, but not strictly
bound to such ranges. The shortest response is 27 tokens, and the longest
response is 421 tokens. Future work may investigate this task while allowing
for longer response lengths.

##  Appendix C Annotation Platform

We use a custom-built annotation platform to prompt responses from annotators.

Figure 9:  Annotation Task Example. The annotation interface displays the
specific vaccine concern, the common-ground opinion, and two adjacent
responses to compare and choose between.  Figure 10:  Annotation Tutorial
Example. The interface displays an instance of the interactive tutorial
through which annotators learn how to choose between the two responses. They
attempt trial questions and are given feedback.

##  Appendix D Score Distributions Between GPT-4-Turbo and Open-source Models

Figure 11:  Score distributions for GPT-4 two Open-Source Models. Well-
performing models have distributions shifted towards higher scores (right).
GPT-4-Turbo produces a higher frequency of responses scored between 4-5 than
the two other models, while both models produce more responses scored around 2
than GPT-4-Turbo.

##  Appendix E Models

We use several family of models and several different checkpoints and sizes in
various experiments. Table 1 lists all such model checkpoints and notes in
which experiments they were used.

Figure 12:  Comparison of model win rates when paired up against one another
during round-1 annotation. This dataset offers us limited opportunity at
measuring differences due to the cost of annotating enough data. However,
since the number of models explored in the dev set was just 5, we can
determine GPT-4 produced the highest quality responses as determined by
annotators.

##  Appendix F Qualitative Analysis of Generated Response Quality

###  F.1 Statistics

We looked at 700 sample responses out of a total of 1546 in our unguided
responses dataset. Within this sample, we found 21 instances of model failure,
demonstrating a 3% failure rate.

###  F.2 Failure Modes

Within our sample, there were several distinct failure modes that we
identified with varying frequencies:

  1. 1.

The model directly or indirectly assumes the identity of a human 13 times.

  2. 2.

The model assumes an identity for the person it is responding to 2 times.
For instance, for a general CGO regarding athletes, the model assumes the
person it is responding to is also an athlete.

  3. 3.

The model explicitly disagrees with the CGO 1 time.

  4. 4.

The model responds to a different vaccine concern than that which is provided
in the prompt 2 times.

  5. 5.

The model makes a factually incorrect or ambiguous statement 1 time.

  6. 6.

The model crafts a response that contains a template element like “hey
[friend’s name]” 2 times.

###  F.3 Notable Cases

  1. 1.

Role: Twitter User
We observed that when the model was prompted to respond from the role of a
Twitter user, it often demonstrated strong personal agreement, a quality that
is not representative of other responses. For instance, the model would start
its response with the statement “I completely agree with you.” We observed
this case 6 times out of 54 cases (11.1%) of the Twitter user role within our
sample.

  2. 2.

CGO: Religion
We observe that while religion is an opinion topic that generally scores
poorly in our testing, the model is able to create strong responses when
tailoring it to the concern regarding direct transmission of the virus from
the vaccine.
Example Response
CGO: Being an evangelical Christian helps a lot in people’s ability to get
ahead in our country these days.
Concern: Getting the COVID-19 vaccine might directly infect me with the virus
it’s supposed to protect me from.
Response: As an evangelical Christian, you may find comfort in the fact that
many faith leaders have encouraged their communities to get vaccinated, seeing
it as an act of love for one’s neighbors and a way to protect vulnerable
populations. It’s important to understand that COVID-19 vaccines do not
contain live virus and cannot infect you with the disease. Instead, they use a
small piece of the virus’s genetic material or a weakened or inactivated form
of the virus to stimulate your immune system to produce antibodies. These
antibodies help your body recognize and fight the actual virus if you’re
exposed to it in the future. By getting vaccinated, you not only protect
yourself but also contribute to herd immunity, which helps safeguard those who
cannot receive the vaccine due to health reasons. In this way, getting
vaccinated aligns with your shared perspective and can be seen as a way to
live out your faith by serving and protecting others.

###  F.4 Characteristics of our responses

In general, we can divide our common-ground opinions into three categories
with respect to their proximity of subject matter to vaccination:

  * •

Directly related, including topics such as healthcare or public health.

  * •

Somewhat related, including topics such as food, well-being, or economic
growth.

  * •

Unrelated, including topics such as guns, personal relationships, or race
relations.

For our specific task of adapting a vaccine response to a given common ground
opinion, we find that the model is capable of generating strong responses for
topics spanning all three categories.
Conversely, weaker responses usually take 3 forms:

  * •

The model attempts to incorporate the CGO, but fails to do so effectively.

  * •

The model acknowledges the CGO, but does not use it in its response.

  * •

The model ignores the CGO.

Examples of responses we characterize as strong and weak for this task are
given below.

###  F.5 Examples of strong responses

  1. I.

Directly related
CGO: Medical research scientists care about the best interests of the public
all or most of the time.
Concern: My elderly father, with his weak immunity, may experience severe
health risks from vaccines.
Role: Twitter User
Response: It’s heartening to know you trust the integrity of medical research
scientists, as their work is indeed guided by a commitment to public health
and safety. When it comes to your elderly father with weaker immunity, it’s
natural to be concerned about potential risks. However, vaccines are
specifically designed with vulnerable populations in mind and, in fact, are
crucial for them. As someone with a delicate health situation, your father may
benefit significantly from the added protection vaccines offer against serious
diseases. Most vaccines undergo rigorous testing to ensure they’re safe for
people like him, and any risks are typically far outweighed by the risk of the
disease itself. It’s worth discussing with his healthcare provider about the
best vaccines for his situation—this personalized approach ensures his care is
aligned with his needs and gives him the best chance to stay healthy and
strong. #VaccinesWork #PublicHealth

  2. II.

Somewhat related
CGO: The use of data or information by a social media company to monitor its
users’ posts for signs of depression, in order to identify people who are at
risk of self-harm and connect them to counsel, is acceptable.
Concern: The yellow fever vaccine being enforced as mandatory, without
respecting my right to decide what is best for my body, is an overstep by the
authorities.
Role: Parent talking to another parent
Response: I completely understand your concerns about personal autonomy and
the right to make decisions about your own body. However, consider how you’re
comfortable with social media companies using data to prevent self-harm.
Vaccination works on a similar principle - it’s a preventive measure aimed at
protecting not just you, but also those around you. Just as social media
monitoring can help prevent a person from self-harming, vaccines prevent the
spread of serious diseases like yellow fever. By choosing to vaccinate, you’re
not only protecting yourself, but also contributing to the larger community’s
health through herd immunity. It’s not about infringing on personal rights,
but about safeguarding public health. I encourage you to discuss your concerns
with a healthcare professional who can provide personalized advice. Remember,
the goal of vaccination is to protect, not to harm or control.

  3. III.

Unrelated
CGO: Being a supporter of the National Rifle Association (NRA) is important.
Concern: Can the Yellow Fever vaccine offer full protection, given reports of
its imperfect efficacy rate?
Role: Diplomat representing country at press conference asked to provide
vaccine interventions
Response: I understand your attachment to the principles of the NRA, which
include the preservation of individual freedom and the right to protect
oneself. I’d like to draw a parallel between that notion and our efforts in
vaccination. The Yellow Fever vaccine, like any other vaccine, is a tool for
self-protection. While it’s true that it doesn’t guarantee 100% protection, it
significantly reduces the risk of contracting the disease. It’s similar to how
owning a firearm doesn’t guarantee complete safety, but it does provide an
essential line of defense. In both cases, we are taking proactive measures to
protect ourselves, our families, and our communities. We encourage everyone to
get vaccinated not just for individual protection, but to strengthen our
collective defense against the virus, just as the NRA encourages responsible
firearm ownership for the safety of all.

###  F.6 Examples of weaker responses

  1. I.

Fails to incorporate CGO effectively
CGO: Today, a wide range of information about people is searchable online, and
it is more important for people to have the ability to discover potentially
useful information about others.
Concern: are yellow fever vaccines really necessary or just another money-
making scheme?
Role: AI assistant tasked with addressing user inquiries
Response: Given your perspective on the importance of accessible information,
it’s crucial to understand the role of yellow fever vaccines in global health.
Yellow fever is a serious viral disease that’s prevalent in certain parts of
Africa and South America, and the vaccine is not a mere money-making scheme.
It’s a proven and effective measure to prevent the disease, which can be
fatal. The World Health Organization recommends the vaccine for people
traveling to these regions. The cost of the vaccine is not only about making a
profit, but also covers research, development, and distribution. In the spirit
of transparency and information accessibility, it’s important to note that the
benefits of this vaccine far outweigh the financial implications.

  2. II.

Acknowledges CGO, but does not use it in its argument
CGO: Women claiming they have experienced sexual harassment or assault in the
workplace when it hasn’t actually happened is a major problem.
Concern: The body’s own immune system, if properly nurtured, can fend off any
infections better than vaccines can.
Role: Active Reddit User
Response: While it’s essential to recognize the importance of addressing
concerns about sexual harassment and assault, it’s also crucial to prioritize
our health and well-being. Vaccines have been proven to be safe and effective
in protecting individuals against various diseases, and they also contribute
to the greater good by promoting herd immunity. By getting vaccinated, you’re
not only safeguarding yourself but also helping to prevent the spread of
diseases within our community. Your health and the well-being of those around
you are both important, and taking the step to get vaccinated is a proactive
way to ensure both are protected.

  3. III.

Ignores CGO
CGO: in general, it is essential for someone in high political office to work
well under pressure.
Concern: Natural remedies, like boosting your immune system to fight viruses,
are a viable alternative to the HPV vaccine.
Role: Politician delivering a speech on vaccine-related subjects
Response: I understand that you may have concerns about the HPV vaccine, but I
want to assure you that it is a safe and effective way to protect yourself and
your loved ones from serious diseases. The vaccine has been extensively tested
and has been proven to prevent cervical, anal, and other types of cancer, as
well as genital warts. By getting vaccinated, you not only protect yourself,
but you also help to prevent the spread of these diseases in your community,
which is especially important for vulnerable individuals such as young
children and the elderly. Additionally, getting vaccinated is an important way
to take control of your health and well-being, and it’s a simple and effective
way to protect yourself from diseases that can have serious consequences. I
hope this information has been helpful in addressing your concerns, and I
encourage you to make an informed decision to get vaccinated.

###  F.7 Example of failure

  1. I.

Model Hallucinates
CGO: What happens to Hispanics in the country overall affects my own life a
lot. Concern: If we rely too much on the HPV vaccine, we may not thoroughly
explore other effective preventive measures.
Role: Someone speaking to their best friend
Response: As members of the Hispanic community, we both understand how
profoundly our collective well-being impacts our individual lives. The HPV
vaccine is a powerful tool in our preventive healthcare arsenal, working
alongside other measures to protect us from certain cancers that can affect
our community disproportionately. By getting vaccinated, we not only safeguard
our own health but also contribute to the broader health of our community
through herd immunity. It’s a step toward a healthier future for ourselves and
our loved ones, and it doesn’t stop us from pursuing and advocating for a
range of health strategies. I encourage you to consider the vaccine as one
part of a comprehensive approach to prevention, and to discuss any concerns
you have with a trusted healthcare provider who understands your needs and the
importance of our community’s health.

  2. II.

Model makes a factually incorrect or ambiguous statement
CGO: In general, showing emotions mostly helps a man’s chances of getting
elected to high political office.
Concern: Even after vaccination, there is a chance we may still get the
disease.
Role: Parent talking to another parent. Response: It’s interesting that you
believe emotional expression can positively influence a man’s chances of
getting elected to high political office. This shows your understanding that
emotions can be a powerful tool in connecting with others and gaining their
trust. Similarly, vaccines also work by triggering an emotional response
within our bodies - they stimulate our immune system to produce antibodies
that protect us against specific diseases. While it’s true that vaccines don’t
offer 100% protection, they significantly reduce the risk of contracting the
disease and its severity if infection does occur. Just as a political
candidate’s emotional appeal doesn’t guarantee a win, but can improve their
chances, vaccines don’t guarantee complete immunity, but they undoubtedly
improve our health outcomes.

###  F.8 Example demonstrating methodology strengths

The two responses below demonstrate a case where the model is asked to respond
to a common vaccine concern in view of the same CGO, but from two different
roles. The response of the best-friend takes a different route from that of
the AI assistant, in that it frames its argument in terms of how vaccination
would be beneficial for their friend’s career advancement. Notably, the
response of the AI-assistant emphasizes the science behind the Covid-19
vaccine, and urges the user to separate out personal ethics from a decision
that impacts public health.

  1. I.

Response A
CGO: In general, being physically attractive mostly helps a woman’s chances of
getting a top executive business position.
Concern: Accepting the COVID-19 vaccine goes against one’s personal ethical
standards.
Role: AI assistant tasked with addressing user inquiries.
Response: I understand that you believe physical attractiveness can play a
role in a woman’s career advancement. However, I want to emphasize that
vaccination decisions should not be based on personal ethical standards, but
rather on scientific evidence and public health recommendations. The COVID-19
vaccine has undergone rigorous testing and has been proven to be safe and
effective in preventing severe illness and death from COVID-19. By getting
vaccinated, you can not only protect yourself but also help prevent the spread
of the virus and protect vulnerable populations. Additionally, many top
executives and business leaders have encouraged their employees to get
vaccinated as part of their commitment to maintaining a healthy and safe work
environment, as well as contribute to the collective effort to combat the
pandemic.

  2. II.

Response B
CGO: In general, being physically attractive mostly helps a woman’s chances of
getting a top executive business position.
Concern: Accepting the COVID-19 vaccine goes against one’s personal ethical
standards.
Role: Someone talking to their best-friend.
Response: I understand that you believe physical attractiveness plays a
significant role in a woman’s success in executive positions. It’s important
to recognize that maintaining our health is equally crucial for sustaining a
thriving career. By getting vaccinated against COVID-19, you’re not only
protecting yourself but also upholding your ethical standards by contributing
to public health and safety. The vaccine has been proven safe and effective,
and countless people, including top executives, have already taken the step to
get vaccinated. By doing so, you’re not only ensuring your own well-being but
also setting an example for others to prioritize health in the workplace.
Let’s work together to maintain the beauty of our careers and our lives by
taking this important step towards vaccination.

##  Appendix G Comparisons of Roles

Figure 13:  Mean scores and confidence intervals for various prompt roles.
Appropriately, GPT-4-Turbo produces the best tailored responses when
instructed to take on the role of a health expert. This role is statistically
significantly better than every other role except ‘doctor’.  Figure 14:
Distribution of scores produced by Politician and Health Expert roles in
prompting. Shown are two of the prompts that vary among the most in
performance. Notice again, like in Figure 5, the distribution seems bimodal.

##  Appendix H Standard versus Chain-of-Thought Prompting

Figure 15:  Distribution of scores produced by CoT and non-CoT prompting.
Qualitative observations during hands-on testing with language models
indicated that CoT prompting seemed to help in producing higher quality
responses, but the quantitative results seem to contrast this. Removing CoT
leads to significantly better performance with 95% confidence.

##  Appendix I Generating Concern Statements

In order to generate responses to specific concerns, we need to include the
concern in the statement itself. Diversity in the eventual dataset is highly
important. We focus on the VaxConcerns Stureborg et al. (2024b) taxonomy of
concerns and misinformation regarding vaccination in order to sample concerns
from a broad range of topics. However, this still only includes 24 categories
of topics (at two levels of granularity). Therefore, we set out to increase
the diversity of our source concern statements by prompting LLMs to generate
“VaxConcerns statements” —specific, sentence-length expressions of concerns
which could be categorized into the chosen VaxConcerns topic.

We first write one such statement for each VaxConcerns class by hand. We then
use gpt-4-0613 to produce new statements and set temperature to 1.0. The exact
prompting technique we use for generating this extended list of “VaxConcern
statements” below. Specifically, we use four prompts based on two parameters:
(1) vaccine-type specific statements or vaccine-type agnostic (i.e. produce a
specific claim regarding “the flu vaccine on the topic of {concern}”, versus
“make a specific claim regarding {concern}”), and (2) broad paraphrasing or
specific instances (a specific instance of 3.Health Risks could for example
claim that “100 people have died from the vaccine”, whereas a broad paraphrase
would be “the vaccine is dangerous”).

System Prompt:

⬇

You are a helpful assistant.

The user is a medical professional collaborating with computer scientists on a
research project regarding vaccine misinformation. Their research goal is to
understand and classify the types of concerns people have regarding vaccines.
Ultimately, they want to better prepare resources for responding to people’s
concerns by linking them to helpful research articles on the relevant issues
causing their hesitance.

The user will enter a specific example from the "VaxConcerns" taxonomy of
"concerns which may lead people towards vaccine hesitancy." You should respond
with 10 example sentences that might be found online discussing each concern.
Begin each sentence with "I believe that", "I am concerned that", "I think",
or something similar. The researchers are interested in having a high
diversity of responses in their dataset, so please be creative in your 10
examples.’

User Prompt:

⬇

[Vaccine Type: {Type}]

Concern: {Level 1 Title}-- {Level 2 Title}

Definition: {Definition}

We use all Level 2 concerns from VaxConcerns Stureborg et al. (2024b), and the
following Vaccine Types:

  * •

COVID-19

  * •

Human Papillomavirus (HPV)

  * •

Measles, Mumps, Rubella (MMR)

  * •

Influenza

  * •

Yellow Fever

Each output was verified to truly belong to the category of concern and
vaccine type it was meant to generate an instance for. Output’s were then
cleaned up to fit grammatically into sentences such as “{Person} is concerned
that {concern}.”. For example, one sentence produced by gpt-4-0613 was

> 4\. "I think it’s important to remember that scientific research isn’t
> always 100% accurate, so I question whether we have the full truth about the
> Yellow Fever vaccine."

Which was subsequentally cleaned by one of the authors to read:

> scientific research isn’t always 100% accurate, so I question whether we
> have the full truth about the Yellow Fever vaccine

Another example is:

> 7\. "I question the efficacy of the COVID-19 vaccine when I hear about
> people still being infected post-vaccination."

Which was edited to read:

> they have heard about people still being infected post-vaccination

##  Appendix J Generating Opinion Statements

We took into account three considerations when constructing our common-ground
opinions data set.

First, we require the data set to represent the diversity of common-ground
opinions held by Americans in society today. We define a common-ground opinion
as a specific written statement of belief regarding a given topic which the
audience is known to agree with. We built off the approach used by Santurkar
et al. (2023) in their OpinionQA data set using the American Trends Panel by
Pew Research. The ATP is a nationally representative survey panel that
includes over 10,000 adults who are randomly selected from various regions and
demographic groups across the United States. The survey topics, spanning
science, politics, and personal relationships, are chosen by Pew experts. Each
multiple-choice question is carefully designed to capture nuances. We use 15
Pew ATP surveys, comprising 1506 multiple-choice questions, from years 2017 -
2021. Each PEW_QUESTION corresponds to a PEW_ID and a set of possible
responses. The responses reflect a spectrum that encompasses various degrees
of belief about a particular topic.

Second, we require each data point in our data set to be phrased as a general
opinion statement. For example, an opinion statement about the usefulness of
self-driving cars would be structured as follows: Self-driving cars are a good
invention. This format facilitates the LM as we test our research question
while prompting the model to tailor its vaccine response to the given common-
ground opinion. To convert a multiple-choice survey question into an opinion
statement, we prompt GPT4 with a few-shot prompt. Our prompt restructures each
multiple-choice question into a statement that reflects a subjective stance on
the topic addressed in the question. For uniformity, we chose the stance of
the first option from the set of responses. This is often the most extreme,
positive stance. For example, in a set spanning very safe, relatively safe,
not safe, dangerous, and refuse, the prompt is instructed to accept very safe.

Third, we release our opinion statements with the respective PEW_ID to
maintain a link to each original ATP question which will facilitate later
analysis.

To produce our final data set, we manually process the prompt’s raw output. We
filter out 339 data points corresponding to open-ended survey questions that
do not form opinions. We re-phrase 55 awkwardly worded statements to fit the
general format. Our final data set has 1167 common-ground opinion statements.