# Making the Implicit Explicit:
Implicit Content as a First Class Citizen in NLP

Alexander Hoyle  Equal contribution. Rupak Sarkar11footnotemark: 1 Pranav Goel
Philip Resnik
University of Maryland
<EMAIL_ADDRESS>
###### Abstract

Language is multifaceted. A given utterance can be re-expressed in equivalent
forms, and its implicit and explicit content support various logical and
pragmatic inferences. When processing an utterance, we consider these
different aspects, as mediated by our interpretive goals—understanding that
“it’s dark in here” may be a veiled direction to turn on a light. Nonetheless,
NLP methods typically operate over the surface form alone, eliding this
nuance.

In this work, we represent language with language, and direct an LLM to
_decompose_ utterances into logical and plausible inferences. The reduced
complexity of the decompositions makes them easier to embed, opening up novel
applications. Variations on our technique lead to state-of-the-art
improvements on sentence embedding benchmarks, a substantive application in
computational political science, and to a novel construct-discovery process,
which we validate with human annotations.111This manuscript is a work in
progress.

##  1 What this paper is not about

Figure 1: Example showing a pair of tweets from legislators along with
generations using our approach. Embeddings over tweets have a high cosine
distance, while embeddings over generations place them much closer.

It is common to “chase benchmarks” in NLP. In this paper, we initially
targeted semantic similarity tasks on a sentence embedding benchmark and found
a method that led to small improvements over a baseline. However, when
investigating our results, an insight led to a refinement of the method and
changed the overall contribution (section 2).

Our initial idea was to use _multiple expressions of the same meaning_ in
order to improve sentence embedding performance. A single sentence is only one
way of expressing a meaning, and in many settings, there is value in
considering alternative ways of communicating that same meaning. For example,
the BLEU score for machine translation evaluation Papineni et al. (2002) works
more effectively with multiple reference translations Madnani et al. (2007).
Dreyer and Marcu (2012) take this observation a step further by using packed
representations to encode and exploit exponentially large numbers of meaning-
equivalent variations given an original sentence.

Inspired by this observation, we investigated whether useful improvements in
sentence representation could be obtained by expanding a sentence’s form with
multiple text representations restating the same content. If every sentence
$s_{i}$ is represented by a set
$S_{i}=\\{s_{i},\tilde{s}_{i,1},\tilde{s}_{i,2},\ldots,\tilde{s}_{i,n}\\}$
consisting of the original utterance and $n$ paraphrases, can we obtain
improved scores on standard sentence-similarity benchmarks? As baseline, we
computed the cosine similarity comparisons between embeddings of the original
sentences $s_{i},s_{j}$, obtained with the state-of-the-art Sentence-T5 Ni et
al. (2022).222For all experiments we use the model sentence-t5-xl;
directionally similar results were observed for the lightweight all-mpnet-
base-v2 Pairwise comparisons for expanded representations $S_{i},S_{j}$, were
scored by concatenating the embedding for $s_{i}$ with the mean of the
embeddings for the $s_{i,*}$,
$\big{[}e(s_{i});\sum_{k}^{n}e(\tilde{s}_{i,k})\big{]}$. Three paraphrases per
input were generated with both a 7B-parameter Alpaca model Taori et al. (2023)
and the OpenAI gpt-3.5-turbo (derived from Ouyang et al., 2022) using a 0-shot
prompt (footnote 17)

Table 1 summarizes our results on a selection of STS and Paraphrase
Classification tasks from the Massive Text Embedding Benchmark (MTEB,
Muennighoff et al., 2022).333 Our Twitter SemEval 2015 dataset varies from
that in MTEB: we use the original task splits from Xu et al. (2015), whereas
MTEB combines the train, validation, and test into a single test set. Our
method, with Sentence-T5, surpasses the current state-of-the-art on four
datasets irrespective of embedding type, and improves over the Sentence-T5
alone in all but one instance. 444Given the modularity of our approach, we
expect that for instances where we there is an absolute improvement over the
Sentence-T5 baseline, substituting the state-of-the-art embedding model would
further improve results. However, the absolute differences are relatively
small and may not justify the computational cost of generation in a real-world
setting.

| Baseline | Sent-T5+Gens
---|---|---
| Sent-T5 | SOTA | Alpaca | GPT-3
SICK-R | 79.98 | 81.70 | 81.46 | 80.49
STS-B | 83.93 | 87.74 | 85.49 | 86.28
STS12 | 79.02 | 79.11 | 76.97 | 79.18
STS13 | 88.80 | 88.93 | 88.44 | 89.18
STS14 | 84.33 | 84.86 | 84.18 | 85.36
STS15 | 88.89 | 89.76 | 89.28 | 89.28
STS16 | 85.31 | 85.96 | 85.15 | 85.23
STS17 | 88.91 | 90.60 | 89.94 | 90.29
Twitter-PC3 | 86.40 | 86.40 | 88.17 | 87.48

Table 1: Results on STS (Spearman’s $\rho$) and Paraphrase Classification
(Average Precision) benchmarks. _Baseline_ embeds texts in each pair with
sentence-t5-xl Ni et al. (2022), _+Gens_ concatenates averaged embeddings of
additional paraphrases generated with zero-shot Alpaca-7B or GPT-3.
Improvements over Sentence-T5 are underlined. SOTA is the best-performing
model on the MTEB benchmark for any embedding type Muennighoff et al. (2022);
bolded items surpass this state-of-the-art. Most increases are relatively
small, except for Twitter-PC. Inspection of those results led to insights that
we exploit in the remainder of the paper.

Interestingly, the consistent and largest absolute difference is on Twitter
Paraphrase Classification Xu et al. (2015), a dataset consisting of noisy
user-generated text. Investigating, we found an important contrast with other
datasets. For the data in benchmarks like SICK-R Agirre et al. (2014), the
language tended to include relatively straightforward assertions like _The cat
chased the mouse_ and the LLM-based method of generating variants produced
additional expressions like _The cat ran_.555We use this example for
illustration but it does not come from any of the datasets.On the other hand,
for Twitter PC, although the prompt was intended to create alternative
expressions of explicit content, we found that the LLM was producing implicit
or inferred content. For example, the tweet But why were people watching the
heat play when 8 mile is on produces But why were people watching the
basketball game instead of the movie 8 Mile? That is, the model injects novel
information into the “paraphrase” that is inferrable from the explicit
content, in this case based on world knowledge: the (Miami) Heat are a
basketball team, therefore watching them play means watching a basketball
game, and _8 Mile_ is a 2002 film. Quantifying this difference informally, we
found that the latter kind of inference resulted 16% of the time for the
Twitter Paraphrase data and 0% for the SICK-R data (significant at
$p<0.05$).666 Specifically, we randomly sampled 50 items and generated
paraphrases with a prompt and text-davinci-003. The nature of the inference
was judged by two authors using an informal rubric, essentially asking if new
information was present in the paraphrase. The agreement rate was 88% and
disagreements were resolved via discussion. The reader can form their own
judgments; see a subset of examples in table 8.

While not a rigorous comparison, validating the strong contrast led us to
think more deeply about _why_ the performance on the Twitter-PC task might
have turned out noticeably different. Our conclusion is that for this
particular task — the nature of the Twitter data together with the goal of
assessing sentence similarity — translating implicit information into explicit
language yields more effective representations for the purpose. Does this core
insight apply to other problems in NLP? Augmenting text with language models
has led to improvements on other tasks, particularly in question answering
(e.g., Mao et al., 2020; Chen et al., 2022; Wu et al., 2023).

In the remainder of the paper, we argue that the answer to this question is
yes: explicit information confers benefits. By using language models to bring
implicit content into the foreground _in text_ , it is possible to take
advantage of that text using existing NLP techniques. We validate the idea by
demonstrating improved performance on two representative problems using text
analysis in computational social science.

##  2 What this paper _is_ about

###  2.1 Implicit content

Language communicates both explicit and implicit content. As a first
approximation, we take implicit content to refer to any information inferred
from, rather than represented in, the semantics of the utterance. Broadly
speaking, we follow (Bach, 2004, p. 476) in distinguishing “information
encoded in what is uttered” from extralinguistic information.

Although we do not employ formal representations here, the semantics of
utterances have traditionally been captured in logical terms, e.g. the
representation of _The black cat meowed_ would generally encode the existence
of a unique contextually-relevant cat, a meowing event of which the cat is the
agent, and a property of blackness being predicated on the cat. However, the
utterance also encodes implicit information not explicitly stated, inferred
from the semantics of the lexical items or their composition. For example,
understanding the meaning of the above sentence also means understanding that
it involves an animal, the color of the animal, and the production of a sound.
These semantic inferences are one kind of implicit content.

In addition, most semanticists would distinguish semantic content of either
kind from information that is inferred on the basis of relevant contingent
facts about the world. This is another form of implicit content; for example,
one can plausibly infer that the cat in the above sentence is a pet, but that
inference has its basis in knowledge about what’s plausible in this
conversational context, not solely in the sentence’s semantics.

Along similar lines, Searle (1965) classically made the distinction between
the semantic or proposition-indicating element—data endogenous to the surface
form—and the function-indicating device of an utterance—its exogenous or
implicit information. Theories of pragmatics identify many other types of
implicit content, such as presuppositions, conventional implicatures,
conversational implicatures, and speech acts. Additional forms of pragmatic
inference pertain not to what is said, but to properties of the speaker, such
as their intentions or beliefs. All of these fit Bach’s Bach (2004)
characterization of pragmatic information as being “relevant to the hearer’s
determination of what the speaker is communicating … generated by, or at least
made relevant by, the act of uttering it”.

###  2.2 Making the implicit explicit

Historically, NLP has been replete with methods to take linguistic utterances
and yield structured representations that include explicit semantic
representations as well as structured representations of implicit content
ranging from sentiment and emotion inventories, to speech acts, to rhetorical
relationships Enzo et al. (2022); Tausczik and Pennebaker (2010) But even
defining what such inventories or representations should look like has been an
enormous challenge in practical terms, and no general-purpose mechanism exists
to generate all of them.

Our informal analysis in Section 1 suggested that text similarity in Twitter
might be a problem space that could benefit from attention to implicit content
and, although not by design, our results drew attention to the fact that LLMs
can be used generatively to bring at least some kinds of implicit content to
the surface in the process of using LLMs to paraphrase. This raises an
intriguing possibility: what if instead of trying to generate and use implicit
content in a structured form, instead we were to explicitly _ask the LLM for_
implicit inferences expressed _as unstructured language_ — making it possible
to deploy the full arsenal of NLP methods on the implicit content just as if
it were explicit?

Such a move is consistent with recent work showing that large language models
(LLMs) can generate implicit information when provided with some utterance:
when appropriately prompted, they can draw specific kinds of logical, factual,
and pragmatic inferences from an input Petroni et al. (2019); Jiang et al.
(2020); Patel and Pavlick (2022); Hu et al. (2022). Our goal here is to take a
similar kind of approach, but to treat these generations as the objects of
study in and of themselves—as first class citizens in NLP.

In designing our approach, we observe that the inherent sparseness of text
data makes it common to represent text using lower-dimensional
representations, which can often be viewed as a kind of decomposition. Topic
models, for example, are useful because they uncover implicit content in the
form of distributions across latent categories. In qualitative content
analysis, the most widespread use of topic models, the implicit categories
themselves are the objects of interest rather than the language itself Hoyle
et al. (2022). Clustering techniques have a similar effect, as does breaking
text out into SVO triples; see Ash et al. (2022) for an approach to
characterizing narratives, another kind of implicit content, exploiting both.

The approach we take, therefore, has two main elements. The first is prompting
an LLM to produce multiple natural language sentences containing implicit
content for a given input text, using a general template that can be adapted
to the specifics of the particular task and setting (section A.1). The second
is to design the template’s instructions and exemplars so that the LLM
expresses the inferences in simple “atomic” language. We call the set of
sentences generated for a given text its _inferential decomposition_ and we
call each individual element of that inferential decomposition a _generation_.
The reduced complexity of the generated items in an inferential decomposition
makes them more amenable to standard embedding techniques, and it also yields
items that are more readily interpretable. See fig. 1 for an illustration.

Figure 2: Results of human evaluation of clustering outputs. Clusters on
generations take significantly less time to label and are more distinctive
($p<0.05$). While comment clusters obtain higher relatedness ratings, they
obtain a lower score in the membership identification task. All differences
are significant at $p<0.05$ except membership identification between comments
and sentences and the evaluation times for sentence and generation clusters.

###  2.3 Method Overview

As discussed, human utterances are often semantically underdeterminate, and
need further lexical content to be added for the propositional content of the
utterance to be complete. Bach (1994)

Given an utterance, our method generates additional semantic content from a
language model that aims to more completely represent the communicative intent
of the speaker. Somewhat more formally, Bender and Koller (2020) define
meaning as “the relation $M\subseteq E\times I$, which contains pairs ($e$,
$i$) of natural language expressions $e$ and the communicative intents $i$
they can be used to evoke.” Setting aside questions of LLMs’ “understanding,”
if we consider $I$ and $E$ to be random variables, then we can further reason
about their mutual information, $\mathcal{I}(E;I)$. For example, it is clear
that the mutual information between expressions and intents conditioned on a
context $c$ will vary: $\mathcal{I}(E;I|C=\text{medical
record})>\mathcal{I}(E;I|C=\text{friendly conversation})$.

Our aim is thus to increase the mutual information between text
representations and communicative intent by augmenting realizations $e$ with
inferential decompositions $p_{j}$,

$\displaystyle\operatorname*{arg\,max}_{x_{c}}\qquad\mathcal{I}(P\cup
E;I|x_{c})$ $\displaystyle P=\\{p_{j}\sim
p_{\text{LM}}(p_{j}|x_{c},e):j=1,\ldots,n\\}$

where $x_{c}$ is a domain-dependent prompt consisting of an instruction and
exemplars (section A.1) We do not maximize the objective directly, as $I$ is
not known; however, improving the correlation between embeddings of the
augmented text and human judgments of the unmodified surface-form similarity
is a (very) indirect optimization of this quantity (section 1).777In the final
version, we will directly evaluate the validity of generated inferences.

##  3 Clustering

$K$ | Method | Silhouette$\uparrow$ | CH$\uparrow$ | DB$\downarrow$
---|---|---|---|---
15 | Comments | 0.052 | 247 | 3.41
Sentences | 0.042 | 219 | 3.74
Generations (ours) | 0.090 | 329 | 3.03
25 | Comments | 0.035 | 172 | 3.28
Sentences | 0.035 | 152 | 3.64
Generations (ours) | 0.096 | 239 | 2.80
50 | Comments | 0.029 | 104 | 3.26
Sentences | 0.042 | 93 | 3.51
Generations (ours) | 0.114 | 153 | 2.73

Table 2: Intrinsic metrics of cluster evaluation. On a random subsample of 10k
comments, sentences, and generations, the intrinsic metrics rank our model
higher both for a fixed number of clusters (bolded) and across clusters
(underlined). CH is the Calinski-Harabasz Index and DB is Davies-Bouldin.

To evaluate the usefulness of our approach, we turn to a substantive task —
the discovery of public opinion on a contentious issue. Intermediate text
representations are useful for interpretive work in the computational social
sciences and digital humanities, where they can be aggregated to help uncover
high-level themes and narratives in text collections Bamman and Smith (2015);
Ash et al. (2022). In a similar vein, we cluster inferential decompositions of
utterances to uncover latent narratives structuring a corpus.

Specifically, we focus on a corpus of public comments concerning the FDA
authorization of COVID-19 vaccinations in children. In a similar latent
argument extraction task, Pacheco et al. (2022), building on content analysis
by Wawrzuta et al. (2021), clustered tweets relating to COVID-19 to facilitate
effective annotation by a group of human experts. Our approach finds
naturalistic opinion labels by virtue of the decomposition in an unsupervised
way, as noted later in the section.

#### Dataset.

We sample 10k comments from the comment section of this
docket888regulations.gov/document/FDA-2021-N-1088-0001 and run our generation
pipeline to obtain a set of 27,848 unique generations at an average of 2.7
generations per utterance.

Our dataset contains comments expressing overlapping opinions, colloquial
language, false beliefs or assumptions about the content or efficacy of the
vaccine, and a general attitude of vaccine hesitancy. Moreover, many comments
are overly long and verbose. These characteristics allow us to investigate
whether inferential decompositions over the space of utterances facilitate a
more efficient and effective discovery of public opinion & concerns regarding
COVID vaccinations for children.

Source |  Cluster 1 |  Cluster 2 |  Cluster 3
---|---|---|---
Decomposition Clusters |  The vaccine may be harmful to children.
Children are vulnerable to the long-term effects of the vaccine.
Vaccines are still under trials and their side effects are unknown.
The vaccine has not been properly tested.
There is not enough data on the vaccine’s long-term effects. |  Natural immunity is better than vaccine-induced immunity.
Natural immunity is important for children to develop.
Natural immunity is more effective.
Natural immunity is better for fighting the covid virus.
People should develop immunities to stay healthy.
Natural immunity should be reflected in science. |  The government is attempting to control citizens.
American rights are being eroded.
The government should investigate the use of
We need to protect our children.
The US values freedom and choice.
Expert Narratives
Wawrzuta et al. (2021) |  The vaccine is not properly tested, it was developed too quickly |  Natural methods of protection are better than the vaccine |  Lack of trust in the government
Crowdworker Label |  Long-term vaccine worries |  Natural Immunity |  Control by government
Table 3: For public commentary datasets about COVID-19, clusters of
inferential decompositions (our approach, top row) align with arguments
discovered independently by Wawrzuta et al. (2021) (middle row). The overlap
is strong despite the commentary coming from different platforms (Government
website & Facebook) and countries (US & Poland). Outside of the exemplars
passed to the LLM (table 6), our approach is also entirely unsupervised
(furthermore, out-of-domain exemplars lead to convergent results). In the
bottom row, we show an illustrative label for the cluster from a crowdworker.

#### Method.

To compare the extent to which broad themes of public opinion emerge
differently in inferential decompositions as opposed to human utterances
themselves, we cluster the corpus of comments and generated decompositions,
varying the number of topics($K$). To guide the LLM generation, two authors
create 31 exemplars from the data that exhibit a mixture of implicit content
types (table 6; in the final version we will relate these types more
systematically to pragmatic inference types).

As a second baseline, we also represent an utterance by its component
sentences. This results in a corpus of 10k comments, 27k generations, and 45k
sentences. Topic models are also a popular tool for discovering latent topics
in a text corpus, and we consider our method a spiritual successor: just as a
topic model represents a document as an admixture of topics, we represent a
document as a combination of inferential decompositions.999In preliminary
experiments, topic model outputs were of mixed quality and difficult to
interpret. In the final version, we will draw a more careful comparison.

#### Automated Evaluation.

Since we lack ground truth labels for which documents belong to which cluster,
we first turn to intrinsic metrics of cluster evaluation. Specifically, we
compute the silhouette score Rousseeuw (1987), Calinski-Harabasz Index
Caliński and Harabasz (1974), and Davies-Bouldin Index Davies and Bouldin
(1979); roughly speaking, they variously measure the compactness and
distinctiveness of clusters. Since metrics can be sensitive to the amount of
data in a corpus (even if operating over the same content), we subsample the
sentences and generations to have the same size as the comments
(10k).101010Results are similar for the silhouette and Davies-Bouildin scores
when we retain all sentences and generations; the Calinski-Harabasz is better
for the sentences.

Clusters of generations dramatically outperform clusters of comments and
sentences across all metrics for each cluster size—in fact, independent of
cluster size, the best scores are obtained by clusters of generations (Table
2).

#### Human Evaluation.

Performance on intrinsic metrics doesn’t necessarily translate to usefulness
in content discovery Manning et al. (2008), so we evaluate the cluster quality
with a human evaluation. After visual inspection, we set $k=15$.

For a given cluster, we show an annotator four related documents and ask for a
free-text label describing the cluster and a 1-5 scale on perceived
“relatedness.” We further perform a membership identification task: an
annotator is shown an unrelated distractor and a held-out document from the
cluster, and asked to select the document that “best fits” the original set of
four.

We recruited 20 participants through Prolific111111prolific.co reporting
English fluency. After two artificially high- and low-quality clusters to help
calibrate scores, each participant reviewed a random sample of ten clusters
from the pool of 45. We paid 3.50 USD per survey and the median completion
time was 17 minutes.

The results from our human study are elaborated in Fig 2. While comment
clusters receive a higher relatedness score, this is likely due to the
inherent coherence of the dataset—there are often several elements of
similarity between any two comments. Indeed, a lower score in the membership
identification task indicates that comment clusters are less distinct.
Moreover, the comprehension time for comments is significantly longer than for
sentences and generations (Eval Time in Fig 2), taking over 50% longer to
read. On the other hand, clusters of generations strike a balance — they
obtain moderately strong relatedness scores, can be understood the quickest,
and are highly distinct.

#### Convergent Validity.

Although further exploration is necessary, we find that our crowdworker-
provided labels can uncover themes discovered from classical expert content
analysis (table 3). For example, two crowdworkers assign labels containing the
text “natural immunity” to the cluster in table 3—this aligns with the theme
natural immunity is effective discovered in Pacheco et al. (2023) and a
similar narrative in Wawrzuta et al. (2021). Meanwhile, this concept does
_not_ appear anywhere in the crowdworker labels for the baseline clusters of
sentences or comments.

##  4 Legislative Co-voting

In section 3, our method of embedding decompositions uncovers implicit
relationships between text items that may have gone undetected by standard
sentence embeddings. In the following section, we further validate our
approach by determining whether the measured similarities between authors’
utterances correspond to their real-world behavior. Specifically, we focus on
the substantive problem of _legislator co-vote prediction_ (Ringe et al.,
2013).

It is common in computational social science to model individuals’ decisions,
and legislative voting behavior is a common area of interest (e.g., Poole and
Rosenthal, 2000). In such models, data about legislators is used to explain
their behavior—including, in particular, their _speech_ Thomas et al. (2006);
Nguyen et al. (2015); Kornilova et al. (2018); Budhwar et al. (2018); Vafa et
al. (2020).121212Other work also incorporates text from other sources, such as
bills Gerrish and Blei (2011); Gu et al. (2014); Kraft et al. (2016); Davoodi
et al. (2020). Although the majority of such models focus on choices at the
individual level, some work has attended to the fact that many if not most
decisions of this kind take place in the context of a _population_ of
individuals. In such settings, additional information about relationships
between individuals may yield additional predictive power and insight. For
example, traditional theories of homophily suggest that shared properties like
party membership, legislative committee assignments, electoral geography, or
gender will increase the likelihood that two legislators will make the same
choices (McPherson et al., 2001; Kirkland, 2012; Clark and Caro, 2013; Baller,
2017; Wojcik, 2018; Fischer et al., 2019). This change of focus from
individuals to pairs defines an emergent shift of research questions from
_voting_ to _co-voting_ (Ringe et al., 2013; Peng et al., 2016; Wojcik, 2018).

###  4.1 Modeling Covotes

We extend the co-voting framework introduced by Ringe et al. (2013) to
incorporate individuals’ _language_ into the model. At a high level, we
operationalize legislator homophily by measuring the similarities of their
embedded speech on social media (here, Twitter).

#### Model Setup.

Following Ringe et al. (2013) and Wojcik (2018), we will begin by modeling the
log odds ratio of the _co-voting rate_ between a pair of legislators $i,j$
using a mixed effects regression, which controls for the random effects of
both actors under consideration. The co-vote rate $\lambda$ is the number of
times the legislators vote the same way — yea or nay — divided by their total
votes in common within some period (e.g., a legislative session).

$\displaystyle\mathbb{E}\left[\log\Big{(}\frac{\lambda_{ij}}{1-\lambda_{ij}}\Big{)}\right]$
$\displaystyle=\beta_{0}+\bm{\beta}_{k}^{\top}\bm{X}_{ij}+a_{i}+b_{j}$ (1)

where $a_{i}$ and $b_{j}$ model random effects for legislator $i$ and $j$.

$\bm{X}_{ij}$ is an $n$-dimensional vector of features, where each feature
captures a type of pairwise relationship between legislators $i$ and $j$.
These can include binary features for state membership, party affiliation, and
committee membership; or continuous features for real-life social network
statistics (Ringe et al., 2013), Twitter connections (Wojcik, 2018), bill co-
sponsorship ties (Kirkland, 2011; Fischer et al., 2019; Gross and Kirkland,
2019), or joint press releases (Desmarais et al., 2015).

#### Language Similarity

The pairwise matrix $\bm{X}_{ij}$ has generally been used in previous work to
model social relationships between legislators $i$ and $j$. Here, we consider
a formulation of language similarity based on our proposed method.

The goal is to represent each legislator using their language in such a way
that we can measure their similarity to other legislators—our informal
hypothesis of the data-generating process is that a latent ideology drives
both vote and speech behavior. Specifically, we follow Vafa et al. (2020) by
incorporating their tweets; our data span the 115th–117th sessions of the US
Congress (2017–2021), and we limit our analysis to the Senate.131313Tweets
from github.com/alexlitel/congresstweets, votes from voteview.com/about.

We further suppose that ideological differences are most evident when
conditioned on a particular issue, such as “the environment” (Bateman et al.,
2017, note that aggregated measures like ideal points mask important variation
across issues). To this end, we first train a topic model with the Twitter
data.141414Text was processed using the toolkit from Hoyle et al. (2021), and
modeled with collapsed Gibbs-LDA Griffiths and Steyvers (2004) implemented in
Mallet McCallum (2002) Two authors independently labeled the topics they
deemed most indicative of ideology, based on the top words and documents from
the topic-word ($\bm{\beta}^{(k)}$) and topic-document ($\bm{\theta}^{(k)}$)
distributions.151515e.g, top words “border, crisis, biden, immigration”
correspond to the politically-charged issue of immigration; the more benign
“tune, live, watch, discuss” covers tweets advertising a media appearance to
followers. Both annotators initially agreed on 92% of labels; disagreements
were resolved via discussion. The final set of 33 topic-words is in table 9.
Then, for each selected topic $k$ and legislator $l$, we select the top five
tweets ${U}^{(k)}_{l}=\\{u^{(k)}_{l,1},\ldots u^{(k)}_{l,5}\\}$, filtering out
those with estimated low probability for the topic,
$\theta^{(k)}_{u_{l}}<0.5$. Finally, we generate a flexible number of
inferential decompositions for the tweets ${P}^{(k)}_{l}$ using Alpaca-7B
Taori et al. (2023). We use two prompts each containing six different
exemplars to increase the diversity of inferences (section A.1). The
collecitons of tweets and decompositions are then embedded with Sentence-
Transformers (all-mpnet-base-v2 from Reimers and Gurevych, 2019a).

To form a text-based similarity measure between legislators $i$ and $j$, we
first compute the pairwise cosine similarity between the two legislators’ sets
of text embeddings,
$s_{\text{cos}}\left({U}^{(k)}_{i}\times{U}^{(k)}_{j}\right)$ (or ${P}$ for
the decompositions). The pairs of similarities must be further aggregated to a
single per-topic measure; we find the $10^{\text{th}}$ percentile works well
in practice (taking a maximum similarity can understate differences, whereas
the mean or median overstates them, likely due to finite sample bias Demszky
et al. 2019). This process creates two sets of per-topic similarities for each
$(i,j)$-legislator pair, $s^{(k)}_{ij}(u),s^{(k)}_{ij}(p)$.

Covariate |  $\hat{\beta}$ (SE) |  $\Delta$BIC
---|---|---
(intercept) | 8.50 (0.09) | –
Sim. Party | 1.18 (0.00) | 306k
Sim. Utterances | 6.85 (0.12) | 3k
Sim. Decompositions | 7.47 (0.17) | 2k

Table 4: Explanatory power of text similarity measures in a model of co-vote
behavior for the 115th Senate (eq. 1). Coefficients $\beta$ are large and
significantly different than zero ($p<0.001$). $\Delta$BIC is the difference
in the BIC for the model with and without that variable; the values indicate
very strong evidence for the inclusion of each variable. For ease of
exposition we model the mean over the per-topic text similarity scores.

#### Results.

Similarities based on inferential decompositions, $s_{ij}(p)$, help explain
the variance in co-vote decisions over and above similarities from the
utterances (tweets) alone, $s_{ij}(u)$, as well as the agreement in party,
table 4. The differences in Bayesian Information Criterion suggest that there
is greater evidence for models that include the utterance-based similarities
Of course simply knowing whether senators’ parties agree is sufficient to
explain most voting behavior: Spearman’s $\rho$ between party and the
agreement rate is above 0.75 for all congresses, and varies between 0.30-0.55
for both text similarity scores (there is little difference between them). In
future work, we will further investigate how the utterances contribute to
changes in estimated legislator similarity, relative to the original tweets
(e.g., fig. 1).

##  5 Related Work

Our work bears a strong similarity to Opitz and Frank (2022), who aim to
increase the interpretability of sentence embeddings through constructing an
AMR graph for a sentence, and retraining Sentence-BERT Reimers and Gurevych
(2019b) with the goal of capturing semantic role features. Although embeddings
obtained through this approach do not outperform the state-of-the-art, their
approach allows an _explanation_ of a particular distance value between two
pieces of text. While this approach makes sentence embeddings more
interpretable, it is ultimately tied to the information present in an
utterance’s surface form (and, moreover, it is unclear whether AMR parsers can
accommodate noisier, naturalistic text).

Our proposed method is not constrained to representing explicit content alone,
and additionally represents inferences large language models have drawn from
the text. LLMs have been used to generate new information ad-hoc in other
settings, for example by augmenting queries Mao et al. (2020) or creating
additional subquestions in question-answering Chen et al. (2022). In Gabriel
et al. (2022), the authors model writer intent from a headline using GPT-2
Radford et al. (2019) and T5-Large Raffel et al. (2019). However, unlike our
approach, they treat these generations as the _end product_ rather than a
starting point.

##  6 Conclusion and Future Work

We have shown that our method of inferential decompositions is useful for
applications where implicit language is most relevant to human understanding.
First, we uncover the high-level narratives in public commentary, which are
often not explicitly expressed in the surface forms. Second, we show that, by
considering the similarity between the implicit content of legislators’
speech, we can better explain their joint voting behavior.

In future work, we plan to additionally fine-tune the embeddings so that they
are more sensitive to the particular use case (e.g., establishing argument
similarity, Behrendt and Harmeling, 2021)). Given that LLMs are known to be
fallible, we will annotate the correctness and plausibility of the generated
inferences. Lastly, we also plan to place the exemplars and a selection of
generations into a formal pragmatic taxonomy, which will help us better
understand the kinds of language for which our method is most useful.

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##  Appendix A Appendix

###  A.1 Prompts and Exemplars

We present our prompts and exemplars in footnotes 17, 6 and 7.

Dataset |  Prompt | Exemplars Per Prompt
---|---|---
STS Paraphrases |  Paraphrase the following text.
###
Text: <input>
Paraphrase: <output> | 0
FDA Comments |  Human utterances contain propositions that may or may not be explicit in the literal meaning of the utterance. Given an utterance, state the propositions of that utterance in a brief list. All generated propositions should be short, independent, and written in direct speech and simple sentences. A proposition consists of a subject, a verb, and an object.
These utterances come from a dataset of public comments on the FDA website
concerning the covid vaccine.
===
Utterance: <input>
Propositions: <output> | 6
Legislative Tweets |  List the claims or beliefs implied by the tweet.
===
Text: <input>
Brief Claims: <output> | 6
Table 5: Prompt templates used for obtaining the decompositions. The FDA Comments and their generations were used in 3, and the generations on legislative tweets were used in 4. We used six exemplars along with the prompts in both of these cases. When generating from Alpaca-7B, we alter these templates according to their format.171717github.com/tatsu-lab/stanford_alpaca Comment |  Exemplars
---|---
Stop illegally forcing the clot shot onto the citizens and their children. This is wrong and you are taking away our freedom. The Covid vaccine is killing people. |  The covid vaccine causes blood clots.
The covid vaccine is being forced on people illegally.
Forcing the covid vaccine limits freedom.
Kids don t get the fake coved flu and then vaccines don t work if you can still gat it. Refer to Colin Powell |  Covid is not real.
Covid vaccines do not prevent covid.
Bodily autonomy is everyone s right and extremely important. everyone should have the right to choose whether or not they want to have a vaccine. It should not be mandated for any profession or for children at any level of education as it violates their rights., |  Vaccine mandates violate bodily autonomy.
Vaccine mandates violates the rights of citizens.
People should choose whether they get vaccinated.
God given freedom over our bodies and mandates aren t laws. Morally wrong., |  God grants people freedom over their bodies.
Mandates are not laws.
Vaccine mandates are morally wrong.
Do not force children to take a dangerous vaccine for a sickness that is nearly nonexistent in that age range and is easily treatable with other medications., |  Covid is nearly nonexistent in children.
Covid is treatable with other medications.
The covid vaccine is dangerous.
Please do not use our children in this massive experiment. Please. Children recover from COVID 19. There is no need for them to be vaccinated against a virus that they will recover from. It doesn t make sense. Please do not use our children in this experiment., |  Covid vaccination is an experiment.
Children do not require covid vaccines.
Covid is not serious is children.
WE DO NOT CONSENT with the emergency authorization of the Covid vaccine for children 5 11., |  The emergency authorization of the vaccine for children is invalid.
NOT to mandate vaccine for children 5 12 as there is no scientific data to prove the vaccines work, |  Scientific data does not support vaccine efficacy.
Please, I beg of you to not pass any mandates that call for children to be vaccinated. It s bad enough that healthy adults who take care of their bodies are being coerced or forced to comply. Our children matter!, |  The government is forcing adults to take the vaccine.
Children should not be mandated to take the vaccine.
Please do not offer vaccinations for kids 5 11. They have beautiful immune systems to keep them healthy and fight off viruses and bacteria., |  Children have strong immune systems.
Children are not susceptible to complications from covid.
Our children need to be protected from experimental vaccines. The proper protocol has not been followed and our children should not be guinea pigs and put it risk. See the Nuremberg trials, you will be held accountable., |  The covid vaccine is experimental.
The proper protocol to approve vaccines was not followed.
Those mandating the vaccine will be held accountable.
The use of covid vaccines in children is criminal.
It appears to me that there is a lack of fidelity in this entire VACCINE process, deemed scientific . There are SO many inconsistencies from the onset of how this vaccine was going to stop covid. From my stand point, half of those I know now have health issues after their shot s are unable to function in their daily routines. I know health professionals, that when asked if they were submitting VAERS info, the reply is We simply do not have the time or Have you ever tried to input Vaers info? It s IMPOSSIBLE!! |  The covid vaccine causes health problems.
Doctors don’t report adverse effects of vaccines.
Doctors are prevented from reporting adverse effects of vaccines.
The covid vaccine approval process is unscientific.
Do not authorize this injection for children. 0.0007 fatality across all kids in America under 18 is no ground for authorizing. |  Child fatality rate is too low to mandate a covid vaccine.
Table 6: Exemplars for inferential decomposition of FDA Comments. We sample
$n$ exemplars from this set to form a prompt, per Table 17.

Tweet |  Exemplars
---|---
My thoughts and prayers go out to the family of former Riverside County Sheriff Larry Smith who passed away late Friday |  Police should be respected
Encourage all of #TN 2 vote in local spring elections & honor the brave men&women who have died so we can be free. |  The military should be revered
People should vote
The military protects democracy
The military protects freedom
.@FreedomHouseDC reports tht #China continues crackdown on media, religious groups + civil society #FreedomReport |  China is authoritarian
China does not support freedom of religion
The Chinese government does not protect freedom of expression
Press freedom is limited in China
Glad the 9th Circuit Court of Appeals banned #DADT- Finally, DADT is over. 13,000 have been discharged under DADT. No more. #equalityforall |  ’Don’t ask, don’t tell’ is a bad policy
Gay people should be able to serve openly in the military
Homosexual people deserve equal rights.
Enjoyed lunch with our remarkable Fall 2017 DC interns. Thank you so much for all of your great work! |  Public service is valuable
Congrats to #Knoxville native Trevor Bayne on becoming the youngest ever winner of the #Daytona500! #NASCAR #VictoryLane |  NASCAR has cultural significance
Tomorrow, #SubCMT will discuss how the sharing economy creates jobs, benefits consumers, raises policy questions: |  The sharing economy has economic benefits
Resources should be shared
Don’t forget to RSVP for our Day of Service in #LynnMA this Sunday using this link $\rightarrow$ #MA6 #ServiceNation & Service and charity are important |
Spoke today @CatoInstitute on importance of #immigration for U.S. prosperity, to watch click |  Immigration strengthens the economy
Immigration benefits the United States
Earlier this week, I met with the owner of @uponadiamond, Steve Brown, to discuss ways this Congress can help our nation’s entrepreneurs. Thanks for stopping by my D.C. office! |  The United States government should support business
Entrepreneurs deserve public support
Exercise your right to vote this Election Day! For #SMCounty constituents, polling info here: |  People should vote
Voting is a right
60 years ago, #RosaParks was arrested for not giving up her bus seat. Her brave act continues to inspire all who fight for #CivilRights. |  Civil rights must be defended
Rosa Parks is a hero
People should be inspired by Rosa Parks
Racial segregation is wrong
Community Colleges like @lavalleycollege are great/affordable places for students to earn the credits for 4 yr degrees #FreeCommunityCollege |  Community college is affordable
Education is important
Community college should be free
Trump’s EO guts climate policies clean energy jobs to boost the profits of corporate polluters #ClimateInactionEO |  President Trump is bad for the environment
Corporations are polluters
Renewable energy creates jobs
President Trump supports corporations
President Trump believes that corporate profits are more important than the
environment
From using the words ‘invasion’ ‘infestation’ to describe immigrants to mocking Asians for their accents, @realDonaldTrump’s racism knows no bounds. Trump’s behavior is beneath the dignity of the office he holds and I wholly condemn his racist remarks. |  President Trump is racist
President Trump dehumanizes immigrants
Immigrants deserve respect
Public leaders should set prosocial examples
If you’re currently near Fort Rucker, please heed warnings from law enforcement. My prayers are with all involved as we await further details. |  People should obey police
Prayer is meaningful

Table 7: Exemplars for inferential decomposition of legislative tweets, used
in the covote prediction task in Section4. We sample $n$ exemplars from this
set to form a prompt, per Table 17.

.

###  A.2 Labeled STS Data

Two authors independently labeled 50 sentences and their generated paraphrases
(from text-davinci-003) for the SICK-R and the Twitter-PC datasets. Due to
space constraints, we only show a selection of the examples (table 8): a
random subset of SICK-R, for which no information is added during paraphrase,
and the examples from Twitter-PC where authors deemed that information _was_
introduced.

Dataset |  Original Text |  Generated Paraphrase | New Info?
---|---|---|---
SICK-R |  A man is rock climbing, pausing and calculating the route |  The man is halting and planning his course while scaling the rock face. | N
SICK-R |  Some cameras are being burned by a person with a blow torch |  A person is using a blow torch to set fire to some cameras. | N
SICK-R |  A cyclist is performing a jump on a bicycle |  A person on a bicycle is executing a leap. | N
SICK-R |  The man on a green bike is performing tricks on the dirt ramp |  The individual on a verdant bicycle is executing stunts on the mud incline. | N
SICK-R |  A white race car is driving through a puddle |  A vehicle of a light color is running through a pool of water. | N
SICK-R |  A person in a red jacket and khaki pants is walking through an American state |  A human being wearing a red outerwear and beige-coloured trousers is striding through a U.S. state. | N
SICK-R |  Eggs are being cracked into a bowl by the woman |  The female is breaking eggs into a container. | N
SICK-R |  Adults and children standing and playing in front of a stair near a wooded area |  Adults and kids congregated around a stairway close to a forested region, having fun. | N
Twitter-PC |  RIP to the Legendary Chris Mac Daddy Kelly of Kris Kross |  Condolences to the iconic Chris Mac Daddy Kelly of the iconic group Kris Kross. | Y
Twitter-PC |  The Thunder are NOTHING wout Westbrook |  The Thunder would be nothing without Russell Westbrook. | Y
Twitter-PC |  Chris Kelly from the rap group Kriss Cross was found dead |  Christopher Kelly of the hip-hop duo Kriss Kross was discovered deceased. | Y
Twitter-PC |  US intelligence agencies believe Chinese ha |  American intelligence services hold the opinion that the Chinese have been engaging in espionage activities. | Y
Twitter-PC |  Backstrom for Min is hurt in warmup |  Minnesotans are dealt a blow as Backstrom is injured during the pre-game warmup. | Y
Twitter-PC |  If the Knicks lose this series I will pick a new team |  If the Knicks fail to win this matchup, I will select another squad to support. | Y
Twitter-PC |  Candice did really good last night |  Candice had an excellent performance last evening. | Y
Twitter-PC |  I fck wit old j cole |  I’m really into the music of J Cole. | Y
Table 8: Selection of STS data and generated paraphrases from text-
davinci-003. Two authors labeled whether paraphrases introduced novel
information. SICK-R: a random selection of examples, for which no paraphrases
included information. Twitter-PC: paraphrases where information _was_
introduced.

###  A.3 Selected Topics

Table 9 shows the top 10 words for each of the 50 topics estimated by a model
over tweets by US senators in the 115th-117th congress, used in the covote
analysis in section 4. Two authors independently reviewed the top words and
five top tweets (not shown) for each topic, then labeled those they believed
were likely to convey information about a legislators’ ideological position.
Authors agreed on 46 labels, and consensus was achieved for the remaining
disagreements following a discussion.

Topic Words | Relevant
---|---
judge supreme_court senate justice nomination court kavanaugh nominee scotus judges | N
vote democracy voting right rights elections people senate act election | Y
tune live watch discuss joining senate today join floor speaking | N
trump president investigation impeachment senate report fbi trial law | Y
federal letter financial protect public congress rule bill new | Y
debt spending democrats bill student trillion tax dollars biden money | Y
infrastructure communities bipartisan funding help jobs bill water broadband | Y
public lands protect national parks state act generations great | Y
energy clean jobs water gas oil climate emissions air | Y
women today work rights years fight justice history country | Y
new great today jobs state space news work proud | N
border crisis biden immigration president southern children trump illegal | Y
climate change crisis action need planet future fossil time fuel | Y
president trump people democrats american political biden want like | Y
drug costs prescription lower care prices health act drugs cost | Y
students school education schools teachers kids work help children | Y
gun violence safety lives background congress action act checks guns | Y
gapol gasen georgia support proud state great mtpol @realdonaldtrump vote | N
afghanistan u.s. president war american biden americans military afghan | Y
forward secretary look hearing today president working senate nomination | N
help disaster state hurricane safe local federal need communities | Y
law justice enforcement police act sexual communities survivors bill | Y
farmers trade iowa producers @chuckgrassley ranchers farm agriculture food | Y
service veterans military thank nation honor women country members | N
senate bill bipartisan passed legislation act house colleagues pass | N
health care help crisis need mental access support opioid | Y
small businesses business help kentucky support local relief ppp | Y
tax families taxes pay class corporations working middle americans | Y
u.s. security israel china taiwan world support allies america | Y
congratulations game luck proud good congrats team great win | N
fight help senate win people need campaign join thank let | N
like know time story people good got going read think | N
relief need families help americans pandemic people workers senate | Y
ukraine russia iran putin war president nuclear u.s. sanctions | Y
family friend life service passing god loved public people | N
great today day thanks year thank state time community | N
rights human people china freedom regime venezuela democracy stand | Y
china companies data security chinese american u.s. government protect | Y
vote today day election ballot voting sure find early netneutrality | N
covid-19 vaccine health pandemic coronavirus covid19 need testing safe | Y
happy day birthday wishing year family celebrating today hope wish | N
women abortion rights right care health protect reproductive life | Y
today violence hate families community lost lives victims loved | Y
day today honor lives service nation women thank men | N
que los para por del las con una más régimen | N
health care americans pre conditions existing people insurance coverage affordable | Y
care child families health family need affordable working plan help | Y
workers jobs wage working work economy union wages pay stand | Y
great today work office thanks week meeting leaders state | N
inflation biden prices energy gas american americans families democrats spending | Y
Table 9: Top topic-word estimates for a dataset of tweets by senators between
the 115th–117th congresses (section 4). Topics are labeled for their likely
ideological relevance. Topics are from Mallet McCallum (2002).