# Improving RAG Retrieval via Propositional Content Extraction: a Speech Act Theory Approach

arXiv: https://arxiv.org/abs/2503.10654 · HTML: https://mlsanity.com/paper/2503.10654/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 19 references that are in the map.
- First posted on arXiv 7 Mar 2025.

## Builds on (most cited first, 5 of 5)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [Unlocking Legal Knowledge with Multi-Layered Embedding-Based Retrieval](https://mlsanity.com/paper/2411.07739/) (2024)

## Built on by (most cited first, 2 of 2)

- [Sensorium Arc: AI Agent System for Oceanic Data Exploration and Interactive Eco-Art](https://mlsanity.com/paper/2511.15997/) (2025)
- [AutoJourn: Multi-Perspective Summarisation, Bias Detection and Bias Neutralisation for LLM-Generated News in Automated Journalism](https://mlsanity.com/paper/2607.18983/) (2026)

## Abstract (arXiv)

When users formulate queries, they often include not only the information they seek, but also pragmatic markers such as interrogative phrasing or polite requests. Although these speech act indicators communicate the usertextquotesingle s intent -- whether it is asking a question, making a request, or stating a fact -- they do not necessarily add to the core informational content of the query itself. This paper investigates whether extracting the underlying propositional content from user utterances -- essentially stripping away the linguistic markers of intent -- can improve retrieval quality in Retrieval-Augmented Generation (RAG) systems. Drawing upon foundational insights from speech act theory, we propose a practical method for automatically transforming queries into their propositional equivalents before embedding. To assess the efficacy of this approach, we conducted an experimental study involving 63 user queries related to a Brazilian telecommunications news corpus with precomputed semantic embeddings. Results demonstrate clear improvements in semantic similarity between query embeddings and document embeddings at top ranks, confirming that queries stripped of speech act indicators more effectively retrieve relevant content.