OpenNovelty.org

Learning Retrieval Models with Sparse Autoencoders

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
text embeddingsparse autoencoderssparse retrievallarge language models
Abstract:

Sparse autoencoders (SAEs) provide a powerful mechanism for decomposing the dense representations produced by Large Language Models (LLMs) into interpretable latent features. We posit that SAEs constitute a natural foundation for Learned Sparse Retrieval (LSR), whose objective is to encode queries and documents into high-dimensional sparse representations optimized for efficient retrieval. In contrast to existing LSR approaches that project input sequences into the vocabulary space, SAE-based representations offer the potential to produce more semantically structured, expressive, and language-agnostic features. By leveraging recently released open-source SAEs, we show that their latent features can serve as effective indexing units for representing documents and queries for sparse retrieval. Our experiments demonstrate that SAE-based LSR models consistently outperform their vocabulary-based counterparts in multilingual and out-of-domain settings. Finally, we introduce SPLARE, a 7B-parameter multilingual retrieval model capable of producing generalizable sparse latent embeddings for a wide range of languages and domains, achieving top results on MMTEB’s multilingual and English retrieval tasks. We also release a more efficient 2B-parameter variant, offering strong performance with a significantly lighter footprint.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes SPLARE, a sparse retrieval approach that uses pre-trained sparse autoencoder (SAE) latent features as indexing units instead of vocabulary tokens. It sits within the SAE-Based Sparse Retrieval Models leaf, which contains only two papers total. This represents a relatively sparse research direction within the broader taxonomy of 50 papers across 36 topics, suggesting the specific combination of SAEs and learned sparse retrieval remains underexplored compared to more established areas like vocabulary-based methods or interpretability-focused SAE applications.

The taxonomy reveals that SAE-Based Sparse Retrieval Models is one of three sibling categories under Learned Sparse Retrieval Systems, alongside Neural Lexical and Vocabulary-Based Sparse Retrieval (2 papers) and Composite Codes and Quantization for Scalable Retrieval (2 papers). The broader field shows substantial activity in interpretability applications (13 papers across 4 leaves) and domain-specific uses (20 papers across 11 leaves), but relatively limited work directly applying SAEs to retrieval tasks. The paper's positioning bridges the interpretability benefits of SAE features with practical retrieval objectives, diverging from purely mechanistic analysis while avoiding traditional vocabulary-space projections.

Among 27 candidates examined, the SPLARE contribution shows one refutable candidate out of 10 examined, while the systematic investigation of latent vocabulary advantages and the multilingual model contributions show no clear refutations across 7 and 10 candidates respectively. The limited search scope means these statistics reflect top-K semantic matches rather than exhaustive coverage. The core architectural innovation of using SAE latents for retrieval appears to have some prior exploration, but the systematic comparison to vocabulary-based methods and the specific multilingual implementation at 7B scale show less direct overlap within the examined candidate set.

Based on the limited literature search of 27 candidates, the work appears to occupy a relatively novel position combining SAE architectures with multilingual sparse retrieval objectives. The taxonomy structure confirms this sits in a sparse research area, though the single refutable candidate for the core contribution suggests some conceptual precedent exists. The analysis cannot determine whether broader literature beyond the top-K matches contains additional overlapping work, particularly in multilingual retrieval or SAE applications to information retrieval.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: learned sparse retrieval with sparse autoencoders. The field encompasses a diverse set of research directions that converge on learning compact, interpretable, and efficient representations. At the highest level, the taxonomy organizes work into branches addressing architectural innovations (Sparse Autoencoder Architectures and Training Methods), mechanistic understanding (Interpretability and Mechanistic Analysis of Neural Representations), retrieval system design (Learned Sparse Retrieval Systems), compression strategies (Adaptive and Compressed Representations for Retrieval), multi-modal extensions (Cross-Modal and Multi-Modal Representation Learning), specialized applications (Domain-Specific Applications of Sparse Autoencoders, Collaborative Filtering and Recommender Systems), and foundational learning paradigms (Unsupervised and Self-Supervised Representation Learning). Representative works such as Gated Sparse Autoencoders[2] and Interpretable Features[3] illustrate how architectural choices and interpretability goals shape the landscape, while Learned Sparse Retrieval[41] and Dense Retrieval Control[32] highlight the practical deployment of these ideas in information retrieval contexts. Several active lines of work reveal key trade-offs between sparsity, interpretability, and retrieval effectiveness. On one hand, methods like Disentangling Dense Embeddings[1] and Decoding Dense Embeddings[48] explore how to impose structure on learned representations to enhance both interpretability and controllability. On the other hand, compression-focused approaches such as Sparse Embedding Compression[7] and Beyond Matryoshka[13] prioritize efficiency and scalability. Within this landscape, Sparse Autoencoders Retrieval[0] sits at the intersection of learned sparse retrieval systems and interpretability research, emphasizing the use of sparse autoencoders to produce retrieval-friendly representations. Compared to Dense Retrieval Control[32], which focuses on steering dense embeddings, and Sparse Latents RAG[10], which integrates sparse latents into retrieval-augmented generation pipelines, the original work appears to prioritize the direct application of sparse autoencoder architectures to retrieval tasks, balancing the need for expressive power with the interpretability benefits of sparsity.

Claimed Contributions

SPLARE: Sparse Latent Retrieval approach using pre-trained SAEs

The authors propose SPLARE, a novel learned sparse retrieval method that replaces the standard vocabulary-based projection with pre-trained sparse autoencoders (SAEs). This enables representing queries and documents as sparse vectors over a latent feature space rather than the LLM vocabulary.

10 retrieved papers
Can Refute
Systematic investigation of latent vocabulary advantages

The authors perform extensive experiments comparing latent vocabulary representations from SAEs against traditional vocabulary-based sparse retrieval across multiple benchmarks, demonstrating improved performance in multilingual and out-of-domain settings.

7 retrieved papers
7B multilingual retrieval model achieving competitive MMTEB results

The authors release SPLARE, a 7-billion parameter multilingual retrieval model supporting over 100 languages that achieves top results on MMTEB retrieval tasks, marking the first LSR model to rival state-of-the-art dense approaches on this benchmark. They also release a 2B-parameter variant.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

SPLARE: Sparse Latent Retrieval approach using pre-trained SAEs

The authors propose SPLARE, a novel learned sparse retrieval method that replaces the standard vocabulary-based projection with pre-trained sparse autoencoders (SAEs). This enables representing queries and documents as sparse vectors over a latent feature space rather than the LLM vocabulary.

Contribution

Systematic investigation of latent vocabulary advantages

The authors perform extensive experiments comparing latent vocabulary representations from SAEs against traditional vocabulary-based sparse retrieval across multiple benchmarks, demonstrating improved performance in multilingual and out-of-domain settings.

Contribution

7B multilingual retrieval model achieving competitive MMTEB results

The authors release SPLARE, a 7-billion parameter multilingual retrieval model supporting over 100 languages that achieves top results on MMTEB retrieval tasks, marking the first LSR model to rival state-of-the-art dense approaches on this benchmark. They also release a 2B-parameter variant.

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