# Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval

Arthur Satouf, Gabriel Ben-Zenou, Benjamin Piwowarski, Habiboulaye

Amadou-Boubacar, Pablo Piantanida

► To cite this version:

Arthur Satouf, Gabriel Ben-Zenou, Benjamin Piwowarski, Habiboulaye Amadou-Boubacar, Pablo Piantanida. Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval. The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, Jul 2025, Padova, France. 10.1145/3726302.3730239. hal-05074220

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# Rational Retrieval Acts: Leveraging Pragmatic Reasoning to
Improve Sparse Retrieval

Arthur Satouf

arthur.satouf(at)gmail.com

Université Paris-Saclay, ILLS, ISIR,

Air Liquide

France, Canada

Gabriel Ben-Zenou*

gabriel.ben-

zenou(at)polytechnique.edu

CentraleSupélec, ILLS, AMIAD

France, Canada

Benjamin Piwowarski

benjamin.piwowarski(at)cnrs.fr

Sorbonne Université, ISIR, CNRS

France

Habiboulaye

Amadou-Boubacar

habiboulaye.amadou-

boubacar(at)airliquide.com

Air Liquide

France

Pablo Piantanida

pablo.piantanida(at)cnrs.fr

MILA - Quebec AI Institute,
Université Paris-Saclay,

CentraleSupélec, ILLS, CNRS

France, Canada

## Abstract

Current sparse neural information retrieval (IR) methods, and to a lesser extent more traditional models such as BM25, do not take into account the document collection and the complex interplay between different term weights when representing a single document. In this paper, we show how the Rational Speech Acts (RSA), a linguistics framework used to minimize the number of features to be communicated when identifying an object in a set, can be adapted to the IR case – and in particular to the high number of potential features (here, tokens). RSA dynamically modulates token-document interactions by considering the influence of other documents in the dataset, better contrasting document representations. Experiments show that incorporating RSA consistently improves multiple sparse retrieval models and achieves state-of-the-art performance on out-of-domain datasets from the BEIR benchmark. https://github.com/arthur-75/Rational-Retrieval-Acts

## CCS Concepts

* Information systems → Novelty in information retrieval; Probabilistic retrieval models; Information retrieval diversity; Document structure; Content analysis and feature selection.

## Keywords

Neural Information Retrieval, Sparse Retrieval, Rational Speech Acts, Pragmatic Reasoning, Linguistic Pragmatics for Retrieval,

## ACM Reference Format:

Arthur Satouf, Gabriel Ben-Zenou, Benjamin Piwowarski, Habiboulaye Amadou-Boubacar, and Pablo Piantanida. 2025. Rational Retrieval Acts: Leveraging Pragmatic Reasoning to Improve Sparse Retrieval. In *Proceedings of the 48th International ACM SIGIR Conference on Research and Development*

in *Information Retrieval (SIGIR '25), July 13–18, 2025, Padua, Italy*. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3726302.3730239

## 1 Introduction

The IR field evolved rapidly since the transformer architecture has been adopted to design various types of models, ranging from effective but less efficient cross-encoders [24] to more efficient models based on dual architectures [11, 16]. Among the latter, sparse neural IR models like e.g. SPLADE [11] are state-of-the-art with a performance well above that of standard IR models like BM25 [26] on most datasets. However, on the document representation side, [21] showed that transformer-based sparse IR model can produce “wacky weights” – i.e., term weights with limited discriminative power. This issue is compounded by the typically small number of query keywords, as well as the relatively low contrastive ability of existing models. Even traditional models such as BM25 do not fully capture the interplay among terms within documents.

Another issue with sparse representations is on the query (user) side. Azzopardi and Zuccon [2] describe users as rational agents when they interact with a search engine: they seek to reduce the number of keywords they type. Such economy-based theories in IR echo the one of Grice in linguistics [15], that focuses on how people convey meaning beyond what is explicitly stated. These ideas have been mathematically formalized in the Rational Speech Acts (RSA) framework, proposed by Frank and Goodman [13]. RSA models human-like communication between a speaker and a listener by assuming a shared common knowledge among pragmatic agents that helps lower the communication cost (number of utterances) while keeping meaning unambiguous.

While users behave as pragmatic speakers [2], existing IR models are not pragmatic listeners [21] – and we argue here that this discrepancy can be reduced using the RSA framework, especially for out-of-domain adaptation. More precisely, we show that applying RSA to IR, to transform literal document representations to pragmatic ones, improves the performance of IR sparse models. Neural networks and modern IR models already capture meaning beyond individual token semantics, for example through attention mechanisms, but, in addition, we distinguish here between literal

*The author conducted this work as an intern at ILLS.

<figure><img src="image_2.png" /></figure>

This work is licensed under a Creative Commons Attribution 4.0 International License.

SIGIR '25, July 13–18, 2025, Padua, Italy

© 2025 Copyright held by the owner/author(s).

ACM ISBN 979-8-4007-1592-1/2025/07

https://doi.org/10.1145/3726302.3730239
representations (before RSA is applied) and pragmatic representations (after RSA refines them). The word pragmatic here is to be understood in the sense of Bunt's concept of pragmatic behavior [4], that is acting in consideration of the context. RSA incorporates such context with the consideration of external information (in our case, all possibly queried documents and all tokens of the vocabulary).

Cohn-Gordon et al. [6, 7] first applied RSA to image captioning and machine translation to reduce ambiguity. In image captioning, RSA refines the output of a language generation model, ensuring the generated caption distinguishes the target image from others in the dataset. In translation, RSA modifies the English translation of a German sentence to ensure unambiguous meaning relative to other sentences in the set of sentences to translate. In both cases, the goal is to produce the most suitable utterance possible regarding the context – which is the set of all possible meanings to be conveyed, i.e., still in Bunt's sense [4], the most pragmatic utterances. In other terms, RSA brings models up to humans pragmatic level. Just as in language tasks, we show in this paper that RSA enhances IR by making document representations more pragmatic.

RSA has also been employed to model various linguistic phenomena [8, 9, 12, 14], and its simplicity and explainability make it a powerful tool for modeling human communicative behavior. However, this simplicity also poses challenges when scaling the RSA to real-world scenarios with significantly larger datasets. To date, only a few works have successfully integrated RSA with AI-driven language models, often requiring adaptations to the RSA mechanism itself [6, 7, 17, 18, 28]. Similarly, applying RSA to IR requires adjustments to handle large lexicons, leveraging sparse IR model outputs to our advantage.

In this paper, we make the following contributions:

* We introduce Rational Retrieval Acts (RRA), an adaptation of the RSA [13] for sparse IR models, carefully extending it to handle a large number of documents (meanings) and tokens (utterances).

* We achieve significant performance improvements in out-of-domain sparse retrieval without increasing inference costs.

## 2 The Rational Retrieval Acts (RRA)

To apply the RSA framework to IR, we map tokens from the vocabulary $\mathcal{T}$ to RSA's utterances and documents from the set $\mathcal{D}$ to RSA's meanings. For clarity, we use the IR terminology (tokens, documents) instead of the RSA's (utterances, meaning).

### 2.1 Integrating the RSA

RSA initially assumes that we can define a positive weight $\mathcal{L}(t, d) \in \mathbb{R}^+$ for each token-document pair $(t, d) \in \mathcal{T} \times \mathcal{D}$. The function $\mathcal{L}$ is called the Initial Lexicon. In this paper, we propose to derive $\mathcal{L}(t, d)$ directly from the weights $w_{t,d}$ predicted by a sparse IR model using the following equation:

$$ \mathcal{L}(t, d) = f(w_{t,d}), \quad (1) $$

where $f$ represents a well chosen initial transformation function.

RSA then defines the literal listener distribution over the documents conditioned on each token, $L_0(d|t)$. It represents how the IR model interprets $t$, indicating how likely each document $d$ is to be relevant if $t$ appears in the query. The formula defining $L_0$

<figure><img src="image_3.png" /><figcaption>Figure 1: Rational Retrieval Acts (RRA) architecture. Document relevance $L_1(d|t)$ is computed via RSA, assuming a pragmatic user (speaker) query. Final ranking uses the dot product between query and document representations (Eq.(10))</figcaption></figure>

also includes a prior term $P(d)$, which can serve the same role as in probabilistic approaches in information retrieval, e.g.[25]. The exact formulation given by RSA is:

$$ L_0(d|t) = \frac{P(d) \cdot \mathcal{L}(t, d)}{\sum_{d' \in \mathcal{D}} P(d') \cdot \mathcal{L}(t, d')}, \quad (2) $$

where we can write the normalizing factor dependent on $t$ as $Z_t^{(0)} = \sum_{d' \in \mathcal{D}} P(d') \cdot \mathcal{L}(t, d')$.

When no prior knowledge about the documents is available, $P(d)$ is constant across all documents, and the literal listener simplifies to the term-normalized weights $w_{t,d}$ from the retrieval model.

Pragmatism is then introduced through the pragmatic speaker $S_1(t|d)$, a distribution over tokens conditioned on documents. This distribution models the likelihood of a pragmatic user selecting a token to describe a given document, assuming they interpret token-document associations similarly to the retrieval model. In other words, users align with the model's learned representation of term-document relationships but refine their choice of tokens based on the whole sets of documents and tokens. RSA introduces a hyper-parameter $\alpha$ to control how pragmatic the user is. We discuss our choice of $\alpha$ in section 3.1. The RSA defines $S_1$ as:

$$ S_1(t|d) = \frac{\exp(\alpha \cdot \log(L_0(d|t)))}{Z_d^{(1)}}, \quad (3) $$

where $Z_d^{(1)}$ is the normalizing factor for a given $d$.

If the pragmatic speaker is a model of the user's behavior when formulating a query, what interests us is how the retrieval information system should react to this human pragmatic behavior. In the RSA, such reaction is modeled with the pragmatic listener $L_1(d|t)$. Its definition in the RSA is the following:

$$ L_1(d|t) = \frac{P(d) \cdot S_1(t|d)}{Z_t^{(1)}}, \quad (4) $$

where $Z_t^{(1)}$ is the normalizing factor for a given $t$.

One could apply RSA in an iterative way, computing a new pragmatic speaker-listener pair on top of the last pragmatic listener. However, previous works on RSA choose not to [6, 7], especially as the $\alpha$ parameter allows already to balance the level of pragmatic depth of RSA.

### 2.2 Leveraging the sparsity in IR

Naively computing speakers' and listeners' distributions with equations (3) and (4) is not possible in IR regarding memory resources
given the large number of documents and terms (both $S_1$ and $L_1$ matrices are of size $|\mathcal{T}| \times |\mathcal{D}|$). To overcome this dimensionality issue, one way is to reduce the size of the possible meanings by excluding documents that are graded with a low similarity score with the query [1, 23]. However, such shrinking method collides with our need to run a fixed RSA for the whole database at once. Cohn Gordon et al. [6, 7] proceed by leveraging the sequential aspect of generated text to make the space of utterances smaller.

Similarly, we propose a technique that relies on the sparsity of the sparse IR models we apply RSA to. Such sparsity means that most of the tokens of the vocabulary do not occur in each document i.e. for most $(t, d) \in \mathcal{T} \times \mathcal{D}$, $w_{t,d} = 0$. The application of the non linear transformation function $f$ posterior to the sparse models thus lead most entries of the RSA $\mathcal{L}(t, d)$ to be equal to $f(0)$. We show in the following that decomposing $\mathcal{L}(t, d)$ into two factors $l_t^{(0)}$ and $l_d^{(0)}$ corresponding respectively to the global importance of a term and a document for all $(t, d)$ pair such that $w_{t,d} = 0$ allows to drastically reduce the required memory storage from $|\mathcal{T}| \times |\mathcal{D}|$ to $|\mathcal{T}| + |\mathcal{D}|$ to which the varying (small) size of the sparse output weight tensor of the IR model needs to be added.

Formally, we first denote $\mathcal{D}_t$ the set of documents where the token $t$ has a non null weight ($w_{t,d} \neq 0$), and similarly, $\mathcal{T}_d$ the set of tokens which have a non-null weight for document $d$. Then, for any token $t$ and document $d$ such as $d \notin \mathcal{D}_t$

$$L_0(d | t) = \frac{\mathbf{P}(d) \cdot f(0)}{\underbrace{\sum_{d' \in \mathcal{D}_t} \mathbf{P}(d') \cdot f(w_{t,d'}) + \sum_{d' \notin \mathcal{D}_t} \mathbf{P}(d') \cdot f(0)}_{l_t^{(0)}}} \times \underbrace{1}_{l_d^{(0)}} \quad (5)$$

Leveraging this initial decomposition, we can then rewrite the equations (3) and (4) for any token $t$ and document $d$ such that $d \notin \mathcal{D}_t$. First, for the pragmatic speaker, we can rewrite equation (3) $\forall t \notin \mathcal{T}_d$:

$$S_1(t | d) = \underbrace{\exp(\alpha \log l_t^{(0)})}_{s_t^{(1)}} \times \underbrace{\exp(\alpha \log l_d^{(0)}) / Z_d^{(1)}}_{s_d^{(1)}}, \quad (6)$$

where the normalizing factor $Z_d^{(1)}$ is computed as

$$Z_d^{(1)} = \sum_{t \in \mathcal{T}_d} \exp(\alpha L_0(d|t)) + \sum_{t \notin \mathcal{T}_d} \exp(\alpha \log(l_t^{(0)} l_d^{(0)})). \quad (7)$$

Based on this reformulation of the pragmatic speaker, we can rewrite the pragmatic listener (4) for all $d \notin \mathcal{D}_t$:

$$L_1(d | t) = \underbrace{s_t^{(1)} / Z_t^{(1)}}_{l_t^{(1)}} \times \underbrace{s_d^{(1)}}_{l_d^{(1)}}, \quad (8)$$

with the normalizing factor $Z_t^{(1)}$ computed as

$$Z_t^{(1)} = \sum_{d' \in \mathcal{D}_t} S_1(d|t) + \sum_{d' \notin \mathcal{D}_t} s_t^{(1)} s_d^{(1)}. \quad (9)$$

Let us insist on the fact that this does not change at all the RSA process – this reformulation work is made for computing complexity reasons, and is necessary when applying RRA.

## 2.3 Scoring documents

The final representation of a document after applying the RSA is $(L_1(d|t))_{t \in \mathcal{T}}$. While RSA alternation between a speaker and a listener can theoretically be run for several iterations, practical applications of Rational Speech Acts (RSA) modeling to human linguistic behavior typically restrict it to a single iteration. Frank [12] shows that $\alpha$ and the number of iterations balance each other out. We thus fix the number of iterations to 1 and detail our choice of $\alpha$ in section 3.1.

Finally, as depicted in Figure 1, following RSA, the score of a given document is given by

$$\text{score}(q, d) = \sum_{t \in \mathcal{T}_d} w_{t,q} \times L_1(d|t) + l_d^{(1)} \times \sum_{t \notin \mathcal{T}_d} w_{t,q} \times l_t^{(1)}, \quad (10)$$

where $w_{t,q}$ is the value for token $t$ of the representation of the query on the IR model vocabulary.

All together, equations (5) to (9) define the computation of the document representations in our Rational Retrieval Acts (RRA) framework. Compared to the original RSA equations ((2) to (4)), this allows for an memory efficient computation of the pragmatic listeners and speakers. At retrieval time, equation (10) allows to leverage inverted indices for fast retrieval, with minor adaptations. RRA can thus be applied to large-scale document collections.

# 3 Experiments

## 3.1 Setup

For our experiments, we implement the RRA pipeline illustrated by Figure 1. The chosen sparse encoders and datasets of documents and queries are detailed in subsection 3.2. Our choice of pre-transformation function is explained in subsection 3.1. RSA requires both a prior and a $\alpha$ parameter to be defined. For the prior, we assume it is uniform over the documents as no specific prior knowledge is given. It translates into $P(d) = \frac{1}{|\mathcal{D}|}$ for all $d \in \mathcal{D}$.

Regarding $\alpha$, as its value tends to infinity, equation 3 makes it clear that the probability density function given by the pragmatic speaker tends to a Dirac. Conversely, as alpha goes to 0, it tends towards a uniform density. In the first case, only one document is given a non-zero probability (equal to 1), making it impossible to distinguish between subsequent documents; in the second case, documents all have the same importance, and thus cannot be distinguished. Prior work in RSA suggests that $\alpha = 1$ gives the best fit between simulated and human behavior. However, in practice, the choice of $\alpha$ may depend on dataset characteristics and retrieval model parameters. Figure 2 shows the behavior of the S-RRA (Spade+RSA) model for different values of $\alpha$: we notice a difference of performance reaching up to 10 points of nDCG@10 on the TREC-COVID dataset depending on the choice of the $\alpha$ parameter. Therefore, it is expected that performance is bounded between these two extremes and that an optimal alpha exists. The performance is however quite stable when varying $\alpha$, and we can predict a close-to-optimal value of $\alpha$ by validating $\alpha$ on few synthetic queries for each dataset. We follow the methodology in [3], and sample 500 documents from a given dataset. For each document, we use LLAMA3-8B [10] to generate a single, relevant query for each document. Specifically, we prompt the model to create
<figure><img src="image_4.png" /><figcaption>Figure 2: Impact of α on S-RRA performance (nDCG@10). Non-optimal α values can significantly degrade results.</figcaption></figure>

a diverse set of queries. This process produces a synthetic information retrieval (IR) dataset consisting of query-document pairs that are considered relevant. This in turns allows us to select the α that maximizes nDCG@10 on the synthetic dataset. Note that this dataset is relatively small, and experiments using the same queries to fine-tune a neural IR sparse model did not give any positive results.

*Pre-transformation function.* Finally, preliminary experiments on MS-Marco have shown that the pre-transformation function $f: x \to 1+x$ was performing the best among the following mappings: $f: x \to x$, $\log(1+x)$, $\exp(x)$, $1+x$, $\lambda \cdot x$, and $\tanh(x)$. We also observed that functions verifying $f(0) \neq 0$ give better results than the rest, likely because this avoids contrasting too much the document weights $L_1(t|d)$.

## 3.2 Baselines and datasets

Given its strong performance, we adopt SPLADE [11] (specifically splade-v3¹) as our main IR model. We evaluate on the BEIR benchmark [29] in a zero-shot setting and, after verifying SPLADE's effectiveness, apply RSA in the same manner to other sparse models (SPARTA [31], BM25, Unicoil [20], DeepImpact [22]). Each model is compared with its RSA-enhanced version (e.g., SPLADE vs. SPLADE+RSA (S-RRA) and SPARTA vs. SPARTA+RSA (SP-RRA)), as shown in Table 1. To implement these methods, we rely on open-source tools such as Anserini, BEIR, and the models' original codebases. We also report the results in Table 2 of SparseEmbbed [19], ColBERT-v2 [27], as well as the performance of BM25 – alone, with a cross-encoder or used with document expansion from docT5query(D2Q) [5] that we copy from [30].

## 3.3 Results

Our experimental results (Table 1) highlight RSA's effectiveness (“-RRA” columns) in enhancing sparse retrieval models across BEIR datasets. The impact is particularly notable in datasets with longer queries relative to document lengths, such as ArguAna (192.98 query words vs. 166.80 document words, ratio = 1.2) and Quora (ratio = 0.83), where RSA significantly improves performance. Conversely, for datasets like NFCorpus (ratio = 0.01) and Touché-2020 (ratio = 0.02), where queries are much shorter, the benefits of RSA

<table><caption>Table 1: Table 1: nDCG@10 across datasets for sparse models and their RSA-enhanced variants (-RRA). Bold: best. Underlined: significant (p<0.05, paired t-test).</caption><thead><tr><th>nDCG@10</th><th>Splade S-RRA</th><th>Sparta SP-RRA</th><th>bm25 bm-RRA</th><th>Unicoil U-RRA</th><th>Deep-Impact RRA</th></tr></thead><tbody><tr><td>ArguAna</td><td>48.9 <u>50.4</u></td><td>37.1 <u>44.1</u></td><td>34.8 <u>38.4</u></td><td>37.4 <u>41.1</u></td><td>31.8 <u>41.4</u></td></tr><tr><td>FiQA-2018</td><td>37.8 <u>38.3</u></td><td>19.8 <u>24.1</u></td><td>23.8 <u>23.9</u></td><td>28.9 <u>29.5</u></td><td>27.8 <u>29.2</u></td></tr><tr><td>NFCorpus</td><td>36.4 <u>36.6</u></td><td>30.1 <u>30.7</u></td><td>32.5 <u>32.7</u></td><td>33.3 <u>33.6</u></td><td>27.1 <u>27.8</u></td></tr><tr><td>Quora</td><td>81.4 <u>84.0</u></td><td>62.5 <u>71.5</u></td><td>78.9 <u>79.1</u></td><td>66.5 <u>77.2</u></td><td>66.0 <u>68.0</u></td></tr><tr><td>SCIDOCS</td><td>15.5 <u>16.6</u></td><td>12.6 <u>14.4</u></td><td>15.8 <u>15.9</u></td><td>14.4 <u>15.9</u></td><td>13.4 <u>14.6</u></td></tr><tr><td>SciFact</td><td>71.6 <u>73.2</u></td><td>59.8 <u>62.4</u></td><td>66.2 <u>67.2</u></td><td>68.6 <u>69.1</u></td><td>59.3 <u>61.9</u></td></tr><tr><td>TREC-Covid</td><td>74.2 <u>74.7</u></td><td>53.8 <u>59.4</u></td><td>65.3 <u>66.8</u></td><td>64.4 <u>65.7</u></td><td>56.2 <u>62.2</u></td></tr><tr><td>Touch2020</td><td>32.2 <u>32.3</u></td><td>17.7 <u>21.6</u></td><td>36.7 <u>37.2</u></td><td>29.1 <u>30.0</u></td><td>26.0 <u>26.8</u></td></tr><tr><td>CQADupStack</td><td>34.5 <u>35.7</u></td><td>27.5 <u>30.4</u></td><td>30.0 <u>31.3</u></td><td>30.2 <u>31.2</u></td><td>28.5 <u>30.4</u></td></tr><tr><td>Mean</td><td>48.1 <u>49.1</u></td><td>35.7 <u>39.8</u></td><td>42.7 <u>43.6</u></td><td>41.4 <u>43.7</u></td><td>37.3 <u>40.3</u></td></tr><tr><td>Point Gain</td><td>+1</td><td>+4.2</td><td>+0.9</td><td>+2.3</td><td>+2.9</td></tr></tbody></table>

are less pronounced. BM25, which already captures global term interplay through IDF, benefits less from RSA since its term-weighting mechanism is inherently corpus-aware. Unlike Splade and Unicoil, Sparta and DeepImpact do use a uniform query token weight and see substantial gains with RSA. By dynamically adjusting token importance, RSA compensates for their lack of contextual query term weighting, enhancing their ability to differentiate between documents effectively. As shown in Table 2, integrating RSA into SPLADE (S-RRA) improves performance on most datasets, with an average nDCG@10 increase of 1 point. We hypothesize that RSA refines token-document interactions, leading to better document contrast. However, its impact is less pronounced on datasets such as NFCorpus, DBPedia, and Touché-2020, where modulating token-document interactions plays a smaller role in retrieval effectiveness.

<table><caption>Table 2: nDCG@10 performance comparison. Our model S-RRA vs state-of-the-art Bold (highest score).</caption><thead><tr><th>nDCG@10</th><th>BM25</th><th>ColBERT v2</th><th>BM25 +CE</th><th>TAS-B</th><th>D2Q</th><th>Sparse Embed</th><th>Splade S-RRA</th></tr></thead><tbody><tr><td>ArguAna</td><td>31.5</td><td>46.3</td><td>31.1</td><td>42.9</td><td>34.9</td><td><strong>51.2</strong></td><td>48.9</td><td>50.4</td></tr><tr><td>CimateFever</td><td>21.3</td><td>17.6</td><td>25.3</td><td>22.8</td><td>20.1</td><td>21.8</td><td>25.0</td><td><strong>27.5</strong></td></tr><tr><td>DBPedia</td><td>31.3</td><td>44.6</td><td>40.9</td><td>38.4</td><td>33.1</td><td><strong>45.7</strong></td><td>44.3</td><td>44.4</td></tr><tr><td>Fever</td><td>75.3</td><td>78.5</td><td><strong>81.9</strong></td><td>70.0</td><td>71.4</td><td>79.6</td><td>80.5</td><td>81.4</td></tr><tr><td>FiQA-2018</td><td>23.6</td><td>35.6</td><td>34.7</td><td>30.0</td><td>29.1</td><td>33.5</td><td>37.8</td><td><strong>38.3</strong></td></tr><tr><td>HotpotQA</td><td>60.3</td><td>66.7</td><td><strong>70.7</strong></td><td>58.4</td><td>58.0</td><td>69.7</td><td>69.5</td><td>70.3</td></tr><tr><td>NFCorpus</td><td>32.5</td><td>33.8</td><td>35.0</td><td>31.9</td><td>32.8</td><td>34.1</td><td>36.4</td><td><strong>36.6</strong></td></tr><tr><td>NQ</td><td>32.9</td><td>56.2</td><td>53.3</td><td>46.3</td><td>39.9</td><td>54.4</td><td>58.3</td><td><strong>58.9</strong></td></tr><tr><td>Quora</td><td>78.9</td><td>85.2</td><td>82.5</td><td><strong>83.5</strong></td><td>80.2</td><td>84.9</td><td>81.4</td><td>84.0</td></tr><tr><td>SCIDOCS</td><td>15.8</td><td>15.4</td><td>16.6</td><td>14.9</td><td>16.2</td><td>16.0</td><td>15.5</td><td><strong>16.6</strong></td></tr><tr><td>SciFact</td><td>66.5</td><td>69.3</td><td>68.8</td><td>64.3</td><td>67.5</td><td>70.6</td><td>71.6</td><td><strong>73.2</strong></td></tr><tr><td>TREC-Covid</td><td>65.6</td><td>73.8</td><td>75.7</td><td>48.1</td><td>71.3</td><td>72.4</td><td>74.2</td><td><strong>74.7</strong></td></tr><tr><td>Touch2020</td><td>36.7</td><td>26.3</td><td>27.1</td><td>16.2</td><td>34.7</td><td>27.3</td><td>32.2</td><td><strong>32.3</strong></td></tr><tr><td>Mean</td><td>44.0</td><td>49.9</td><td>49.5</td><td>43.7</td><td>45.3</td><td>50.9</td><td>52.0</td><td><strong>53.0</strong></td></tr></tbody></table>

*Case Study: SPARTA on SciFact.* In the SciFact dataset using the SPARTA model, we observe that without RSA, the token “and”–and other common stopwords–can receive disproportionately high weights. For example, in the first document, “and” is assigned a weight of 1.2515, which is higher than more informative tokens such as “statistics” 1.1672 and “evolution” 1.0842. After applying RSA, this pattern is reversed: “and” is significantly down-weighted

<footnote n="1">¹https://huggingface.co/naver/splade-v3</footnote>
to 0.00196, while **"statistics"** and **"evolution"** are promoted to
0.00633 and 0.00467 respectively—making **"and"** roughly three
times less prominent. This example highlights how RSA suppresses
frequently occurring, less informative tokens across the collection,
while amplifying the weights of more distinctive, content-rich to-
kens. This dynamic adjustment improves the contrastiveness and
discriminative power of sparse representations.

4 Discussion

In this paper, we introduce an adaptation of the Rational Speech
Acts (RSA) framework for Information Retrieval (IR), which we call
the Rational Retrieval Acts (RRA). This framework is compatible
with any sparse IR model; here, we specifically apply it to four
state-of-the-art sparse neural IR models and BM25. Our results
demonstrate that RRA enhances models effectiveness without com-
promising efficiency on out-of-domain datasets. A limitation is that
RRA's offline RSA phase must be re-applied to the entire collection,
including new documents, whenever the dataset is updated – but
approximate updates (only computing the document representa-
tions) can be used in this case. Finally, our work also underlines
that document representations, as computed by sparse neural IR
models, might not be optimal at a collection level, and poses the
question of computing – especially for out-of-domain datasets –
better representations that are collection-dependent.

Acknowledgments

The authors acknowledge Air Liquide for the financial support and
ANR - FRANCE (French National Research Agency) for its financial
support of the GUIDANCE project n°ANR-23-1AS1-0003.

References

[1] Jacob Andreas and Dan Klein. Reasoning about pragmatics with neural listeners and speakers, 2016. URL https://arxiv.org/abs/1604.00562.

[2] Leif Azzopardi and Guido Zuccon. Building economic models of human computer interaction. In *Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems*, CHI EA '19, page 1–4. New York, NY, USA, 2019. Association for Computing Machinery. ISBN 9781450359719. doi: 10.1145/3290607.3298809. URL https://doi.org/10.1145/3290607.3298809.

[3] Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. InPars: Unsupervised Dataset Generation for Information Retrieval. In *Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval*, SIGIR '22, pages 2387–2392. Association for Computing Machinery, 2022. ISBN 978-1-4503-8732-3. doi: 10.1145/3477495.3531863. URL https://doi.org/10.1145/3477495.3531863.

[4] H. Bunt. Computational pragmatics. *Oxford University Press*, 2017. URL https://doi.org/10.4312/slo2.0.2023.1.189-217.

[5] David R. Cheriton. From doc2query to docttttquery. In *From doc2query to docTTTTTquery*, 2019. URL https://api.semanticscholar.org/CorpusID:208612557.

[6] Reuben Cohn-Gordon and Noah Goodman. Lost in machine translation: A method to reduce meaning loss, 2019. URL https://arxiv.org/abs/1902.09514.

[7] Reuben Cohn-Gordon, Noah Goodman, and Christopher Potts. Pragmatically informative image captioning with character-level inference. *arXiv preprint arXiv:1804.05417*, 2018.

[8] J Degen, M H Tessler, and N D Goodman. *Wonky worlds: Listeners revise world knowledge when utterances are odd*. 2015.

[9] Judith Degen. The rational speech act framework. *Annu. Rev. Linguist.*, 9(1): 519–540, January 2023.

[10] Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song,

Danielle Pintz, Danny Livshits, David Esiobu, Dhruv Choudhary, Dhruv Maha-
jan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab
AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic,
Frank Zhang, Gabrielle Lee, Georgia Lewis Anderson, Graeme
Nail, Gregoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah
Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanar Arrieta Ibarra, Isabel
Kloumann, Ishan Misra, Ivan Evtimov, Jade Copet, Jaewon Lee, Jan Geffert, Jana
Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer
Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen
Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca,
Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Kartikeya
Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini,
Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Lauren Rantala-Yeary,
Laurens van der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin,
Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira,
Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marcin
Kardas, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike
Lewis, Min Si, Mitesh Kumar Singh, Mona Hassan, Naman Goyal, Narjes Torabi,
Nikolay Bashlykov, Nikolay Bogoychev, Niladri Chatterji, Olivier Duchenne,
Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic, Peter
Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura,
Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ra-
mon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Raileanu, Rohit
Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross
Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabas-
appa, Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang
Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhos-
ale, Shun Zhang, Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten
Sootla, Stephane Collot, Suchin Gururangan, Sydney Borodinsky, Tamar Her-
man, Tara Fowler, Tarek Sheasha, Thomas Georgiou, Thomas Scialom, Tobias
Speckbacher, Todor Mihaylov, Tong Xiao, Ujjwal Karn, Vedanuj Goswami, Vibhor
Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do, Vish
Vogeti, Vladan Petrovic, Weiwei Chu, Wenhan Xiong, Wenyin Fu, Whitney Meers,
Xavier Martinet, Xiaodong Wang, Xiaoqing Ellen Tan, Xinfeng Xie, Xuchao Jia,
Xuewei Wang, Yaelle Goldschlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen
Song, Yuchen Zhang, Yue Li, Yuning Mao, Zacharie Delpierre Coudert, Zheng
Yan, Zhengxing Chen, Zoe Papakipos, Aaditya Singh, Aaron Grattafiori, Abha
Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva
Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alex Vaughan, Alexei
Baevski, Allie Feinstein, Amanda Kallet, Amit Sangani, Anam Yunus, Andrei Lupu,
Andres Alvarado, Andrew Caples, Andrew Gu, Andrew Ho, Andrew Poulton,
Andrew Ryan, Ankit Ramchandani, Annie Franco, Aparajita Saraf, Arkabandhu
Chowdhury, Ashley Gabriel, Ashwin Bharambe, Assaf Eisenman, Azadeh Yaz-
dan, Beau James, Ben Maurer, Benjamin Leonhardi, Bernie Huang, Beth Loyd,
Beto De Paola, Bhargavi Paranjape, Bing Liu, Bo Wu, Boyu Ni, Braden Hancock,
Bram Wasti, Brandon Spence, Brani Stojkovic, Brian Grantido, Britt Montalvo,
Carl Parker, Carly Burton, Catalina Mejia, Changhan Wang, Changkyu Kim,
Chao Zhou, Chester Hu, Ching-Hsiang Chu, Chris Cai, Chris Tindal, Christoph
Feichtenhofer, Damon Civin, Dana Beaty, Daniel Kreymer, Daniel Li, Danny
Wyatt, David Adkins, David Xu, Davide Testuggine, Delia David, Devi Parikh,
Diana Liskovich, Didem Foss, Dingkang Wang, Duc Le, Dustin Holland, Edward
Dowling, Eissa Jamil, Elaine Montgomery, Eleonora Presani, Emily Hahn, Emily
Wood, Erik Brinkman, Esteban Arcaute, Evan Dunbar, Evan Smothers, Fei Sun,
Felix Kreuk, Feng Tian, Firat Ozgenel, Francesco Caggioni, Francisco Guzmán,
Frank Kanayet, Frank Seide, Gabriela Medina Florez, Gabriella Schwarz, Gada
Badeer, Georgia Swee, Gil Halpern, Govind Thattai, Grant Herman, Grigory
Sizov, Guangyi, Zhang, Guna Lakshminarayanan, Hamid Shojanazeri, Han Zou,
Hannah Wang, Hanwen Zha, Haroun Habeeb, Harrison Rudolph, Helen Suk,
Henry Aspegren, Hunter Goldman, Ibrahim Damlaj, Igor Molybog, Igor Tufanov,
Irina-Elena Veliche, Itai Gat, Jake Weissman, James Geboski, James Kohli, Japhet
Asher, Jean-Baptiste Gaya, Jeff Marcus, Jeff Tang, Jennifer Chan, Jenny Zhen,
Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang, Joe
Cummings, Jon Carvill, Jon Shepard, Jonathan McPhie, Jonathan Torres, Josh
Ginsburg, Junjie Wang, Kai Wu, Kam Hou U, Karan Saxena, Karthik Prasad,
Kartikay Khandelwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan,
Kelly Michelena, Keqian Li, Kun Huang, Kunal Chawla, Kushal Lakhotia, Kyle
Huang, Lailin Chen, Lakshya Garg, Lavender A, Leandro Silva, Lee Bell, Lei
Zhang, Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian
Khabsa, Manav Avalani, Manish Bhatt, Maria Tsimpoukelli, Martynas Mankus,
Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev, Maxim Naumov,
Maya Lathi, Meghan Keneally, Michael L. Seltzer, Michal Valko, Michelle Re-
strepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey,
Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Mu-
nish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata
Bawa, Nayan Singhal, Nick Egebo, Nicolas Usunier, Nikolay Pavlovich Laptev,
Ning Dong, Ning Zhang, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar
Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji, Pedro
Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant
Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Rodriguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang, Rohan Maheswari, Russ Howes, Ruty Rinott, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Verma, Seiji Yamamoto, Sharadh Ramaswamy, Shaun Lindsay, Shaun Lindsay, Sheng Feng, Shenghao Lin, Shengxin Cindy Zha, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satterfield, Sudarshan Govindaprasad, Sumit Gupta, Sungmin Cho, Sunny Virk, Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Kohler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews, Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Kumar, Vishal Mangla, Vitor Albiero, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang, Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiaofang Wang, Xiaojian Wu, Xiaolan Wang, Xide Xia, Xilun Wu, Xinbo Gao, Yanjun Chen, Ye Hu, Ye Jia, Ye Qi, Yenda Li, Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang, Yuchen Hao, Yundi Qian, Yuzi He, Zach Rait, Zachary DeVito, Zef Rosnbrick, Zhaoduo Wen, Zhenyu Yang, and Zhiwei Zhao. The llama 3 herd of models, 2024. URL https://arxiv.org/abs/2407.21783.

[11] Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant. Towards Effective and Efficient Sparse Neural Information Retrieval. *ACM Transactions on Information Systems*, page 3634912, 2024. ISSN 1046-8188, 1558-2868. doi: 10.1145/3634912. URL https://dl.acm.org/doi/10.1145/3634912.

[12] Michael C Frank. Rational speech act models of pragmatic reasoning in reference games. 2016.

[13] Michael C. Frank and Noah D. Goodman. Predicting pragmatic reasoning in language games. *Science*, 336:998 – 998, 2012. URL https://api.semanticscholar.org/CorpusID:26797288.

[14] Noah D Goodman and Michael C Frank. Pragmatic language interpretation as probabilistic inference. *Trends Cogn. Sci.*, 20(11):818–829, November 2016.

[15] H. P. Grice. Logic and conversation, 1975. URL https://doi.org/10.1016/j.iswa.2022.00118.

[16] Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin, and Allan Hanbury. Efficiently teaching an effective dense retriever with balanced topic aware sampling, 2021. URL https://arxiv.org/abs/2104.06967.

[17] Hyunwoo Kim, Byeongchang Kim, and Gunhee Kim. Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness. In *Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*, pages 904–916, Online, 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.emnlp-main.65.

[18] Hyunwoo Kim, Byeongchang Kim, and Gunhee Kim. Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes. In *Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*, pages 2227–2240, Online and Punta Cana, Dominican Republic, 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.emnlp-main.170.

[19] Weize Kong, Jeffrey M. Dudek, Cheng Li, Mingyang Zhang, and Mike Bendersky. Sparseembed: Learning sparse lexical representations with contextual embeddings for retrieval. In *Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23)*, 2023.

[20] Jimmy Lin and Xueguang Ma. A few brief notes on deepimpact, coil, and a conceptual framework for information retrieval techniques, 2021. URL https://arxiv.org/abs/2106.14807.

[21] Joel Mackenzie, Andrew Trotman, and Jimmy Lin. Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation, 2021. URL http://arxiv.org/abs/2110.11540.

[22] Antonio Mallia, Omar Khattab, Nicola Tonellotto, and Torsten Suel. Learning passage impacts for inverted indexes, 2021. URL https://arxiv.org/abs/2104.12016.

[23] Will Monroe, Robert X.D. Hawkins, Noah D. Goodman, and Christopher Potts. Colors in Context: A Pragmatic Neural Model for Grounded Language Understanding. *Transactions of the Association for Computational Linguistics*, 5:325–338, 2017. ISSN 2307-387X. doi: 10.1162/tacl_a_00064. URL https://direct.mit.edu/tacl/article/43470.

[24] Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. Multi-stage document ranking with bert, 2019. URL https://arxiv.org/abs/1910.14424.

[25] Jay Ponte and W Croft. A language modeling approach to information retrieval. In *ACM SIGIR*, 1998.

[26] Stephen Robertson and Hugo Zaragoza. The probabilistic relevance framework: Bm25 and beyond. *Found. Trends Inf. Retr.*, 3(4):333–389, April 2009. ISSN 1554-0669. doi: 10.1561/1500000019. URL https://doi.org/10.1561/1500000019.

[27] Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. Colbertv2: Effective and efficient retrieval via lightweight late interaction, 2022. URL https://arxiv.org/abs/2112.01488.

[28] Sheng Shen, Daniel Fried, Jacob Andreas, and Dan Klein. Pragmatically Informative Text Generation, 2019.

[29] Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. Beir: A heterogenous benchmark for zero-shot evaluation of information retrieval models, 2021. URL https://arxiv.org/abs/2104.08663.

[30] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-attention distillation for task-agnostic compression of pretrained transformers, 2020. URL https://arxiv.org/abs/2002.10957.

[31] Tiancheng Zhao, Xiaopeng Lu, and Kyusong Lee. Sparta: Efficient open-domain question answering via sparse transformer matching retrieval, 2020. URL https://arxiv.org/abs/2009.13013.

Received 23 January 2025; revised 23 January 2025; accepted 4 April 2025