[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81716-en":3,"doc-seo-81716-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},81716,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Aligning Sentence Embeddings to Human Concepts via Sparse Autoencoders","Dense sentence embeddings power modern Retrieval-Augmented Generation (RAG) systems but remain hard to interpret because their features overlap, producing polysemantic representations that are difficult to analyze or control. The work introduces Top-k Sparse Autoencoders to decompose dense embeddings from sentence transformers (e.g., E5) into sparse, human-interpretable concepts spanning semantic, syntactic, and pragmatic categories. An activation steering method clamps selected latent features to intervene at retrieval time, re-ranking results toward user constraints without retraining.","arXiv :2607 .00023v 1 [ cs .IR] 19 Jun 2026  \nALIGNING SENTENCE EMBEDDINGS  \nTO HUMAN CONCEPTS VIA SPARSE AUTOENCODERS  \nWonseok Shin, Songkuk Kim  \nYonsei University  \n{wonseok.shin,[songkuk](songkuk}@yonsei.ac.kr)[}](songkuk}@yonsei.ac.kr)[@yonsei.ac.kr](songkuk}@yonsei.ac.kr)  \nABSTRACT  \nDense sentence embeddings are fundamental to modern Retrieval-Augmented Generation (RAG) systems but suffer from a lack of interpretability due to feature superposition. This opacity hinders the alignment of retrieval processes with human intent, as the entangled representations are difficult to analyze or control. In this work, we propose a method to disentangle the dense representations of sentence transformers (e.g., E5) into humaninterpretable concepts using Top-k Sparse Autoencoders (SAEs) . We demonstrate that these disentangled features align with specific semantic, syntactic, and pragmatic categories. Furthermore, we introduce an activation steering mechanism that allows for precise intervention in the retrieval process. By clamping specific latent features, we show that it is possible to re-rank search results to better align with user constraints without retraining the backbone model. Our findings suggest that SAE-based decomposition offers a viable path toward transparent and steerable neural information retrieval.  \n1 INTRODUCTION  \nNeural sentence embeddings serve as the backbone of modern retrieval-centric applications, including Retrieval-Augmented Generation (RAG) pipelines (Lewis et al., 2020), yet they suffer from limited interpretability (Lipton, 2018) . According to the superposition hypothesis, dense vectors often compress sparse features into fewer dimensions, rendering them polysemantic (Arora et al., 2018; Elhage et al., 2022) . This opacity creates a fundamental alignment gap (Luan et al., 2021), making it difficult to verify or intervene when retrieval processes diverge from human intent.  \nTo bridge this gap, we propose applying Sparse Autoencoders (SAEs) (Ng et al., 2011) to the final output of sentence encoders, such as the E5 model (Wang et al., 2022) . While SAEs are typically used to study internal LLM residual streams (Bricken et al., 2023; Cunningham et al., 2023), their potential to interpret dense retrievers remains underexplored. Specifically, we utilize a Top-k SAE architecture (Gao et al., 2024) to map dense embeddings into a higher-dimensional, sparse latent space. This methodology allows us to decompose entangled representations into monosemantic features that are human-interpretable, expanding the 1,024-dimensional input into a significantly larger latent space to resolve feature superposition.  \nIn this work, we demonstrate that Top-k SAEs can effectively align dense sentence embeddings with human conceptual frameworks. Our experiments on the WikiText-103-v1 dataset show that the learned latent features correspond to granular concepts. Furthermore, we go beyond static analysis to demonstrate semantic steering. By manually clamping specific latent neurons, we show that it is possible to control the retrieval mechanism—filtering out unwanted concepts without retraining the backbone model. This capability addresses the limitations of macro-steering by enabling micro-level control over semantic features. This work suggests that SAE-based decomposition is a viable path toward transparent, aligned, and steerable neural search systems.  \n2 METHODOLOGY  \nWe propose a framework to disentangle and align the dense representations of neural retrievers using a Topk Sparse Autoencoder (SAE) . This approach maps entangled dense embeddings into a high-dimensional sparse latent space where individual dimensions represent monosemantic concepts.  \n2. 1 TOP-K SPARSE AUTOENCODER ARCHITECTURE  \nWe adopt the Top-k SAE architecture to overcome the limitations of traditional L 1-regularized autoencoders. While L1 penalties effectively induce sparsity, they often introduce a shrinkage bias that suppresses feature activatio","cbCaisqBWb88LvXZ","https://ap.wps.com/l/cbCaisqBWb88LvXZ","pdf",528610,4,1,13,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Top-k Sparse Autoencoder Architecture\n## Latent Feature Steering via Clamping\n## Automated Feature Annotation Pipeline","[{\"question\":\"Why are dense sentence embeddings difficult to align with human intent in RAG systems?\",\"answer\":\"Dense vectors compress sparse signals into overlapping dimensions, creating polysemantic representations. This opacity makes it hard to verify or intervene when retrieval deviates from what users actually intend.\"},{\"question\":\"How does the Top-k Sparse Autoencoder improve interpretability of sentence transformer embeddings?\",\"answer\":\"It maps dense embeddings into a higher-dimensional sparse latent space where only the k most active neurons fire. The resulting sparse features correspond to monosemantic, human-interpretable concepts.\"},{\"question\":\"What is activation steering via clamping, and how does it affect retrieval?\",\"answer\":\"Activation steering clamps selected latent neurons by zeroing specific feature activations. The modified embedding is then used for similarity search, enabling inference-time re-ranking toward user constraints without fine-tuning the backbone.\"}]",1784175597,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"aligning-sentence-embeddings-to-human-concepts-via-sparse-autoencoders","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/aligning-sentence-embeddings-to-human-concepts-via-sparse-autoencoders/81716/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are dense sentence embeddings difficult to align with human intent in RAG systems?","Question",{"text":75,"@type":76},"Dense vectors compress sparse signals into overlapping dimensions, creating polysemantic representations. This opacity makes it hard to verify or intervene when retrieval deviates from what users actually intend.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the Top-k Sparse Autoencoder improve interpretability of sentence transformer embeddings?",{"text":80,"@type":76},"It maps dense embeddings into a higher-dimensional sparse latent space where only the k most active neurons fire. The resulting sparse features correspond to monosemantic, human-interpretable concepts.",{"name":82,"@type":73,"acceptedAnswer":83},"What is activation steering via clamping, and how does it affect retrieval?",{"text":84,"@type":76},"Activation steering clamps selected latent neurons by zeroing specific feature activations. 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