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The work replaces this free-form API extractor with a local, deterministic probe trained on hidden states of a small open-source language model. Across 2556 MultiHop-RAG queries, the probe achieves 90.9% set-exact accuracy versus 88.0% for a model-free substring baseline and 80.9% for GPT-3.5, with gains largely from null-query abstention. The fixed 49-source vocabulary prevents allow-list drift, and the model remains computationally cheap, enabling deployment without API calls.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/probe-dont-prompt-a-hidden-state-probe-for-metadata-filtering-in-multi-meta-rag/327801/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/probe-dont-prompt-a-hidden-state-probe-for-metadata-filtering-in-multi-meta-rag/327801.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":56},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does Multi-Meta-RAG address in multi-hop question answering?","Question",{"text":112,"@type":113},"It tackles retrieval failures on multi-hop queries by filtering a vector store using news-source metadata so evidence from multiple documents can be assembled more reliably.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper replace GPT-3.5-based metadata extraction?",{"text":117,"@type":113},"It trains a local deterministic probe on hidden states of a small open-source language model to predict the source labels from a fixed 49-source vocabulary.",{"name":119,"@type":110,"acceptedAnswer":120},"Why is the probe less prone to errors than the prompted GPT-3.5 extractor?",{"text":121,"@type":113},"Because the probe’s output space is exactly the allow-list of 49 sources, it cannot drift to off-list labels that the prompted model sometimes produces.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},327801,1790464664,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":56,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":24,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","arXiv :2607 .03929v 1 [ cs .CL] 4 Jul 2026  \nProbe, Don’t Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAG  \nMykhailo Poliakov and Nadiya Shvai  \nNational University of Kyiv-Mohyla Academy  \nKyiv, Ukraine  \n[mykhailo.poliakov@ukma.edu.ua](mykhailo.poliakov@ukma.edu.ua), [n.shvay@ukma.edu.ua](n.shvay@ukma.edu.ua)  \nAbstract  \nMulti-Meta-RAG improves retrieval for multi-hop question answering by filtering a vector store on metadata (the news source) that it extracts from each query by prompting gpt-3 .5-turbo. We show this proprietary, free-form extractor can be replaced by a local, deterministic probe trained on the hidden states of a small open-source language model. On all 2556 MultiHop-RAG queries the probe reaches 90.9% set-exact accuracy against 88.0% for a model-free substring baseline and 80.9% for GPT-3.5, a margin that comes entirely from null queries, on which GPT-3.5 never abstains; on non-null queries all three stay within about a point. Because the probe’s output space is exactly the fixed 49-source vocabulary, it cannot drift outside the allow-list as the prompted model does. Three design choices make it work: selecting a shallow layer, mean pooling, and class-imbalance-aware multi-label training over the long tail of sources. A 135M-parameter model lands within ∼ 1.5 points of a 1.5B one, so the filter is cheap to output: a partial forward pass through the first few layers plus one linear head, with no API. The code is available at  \n[https://github.com/mxpoliakov/Multi-Meta-RAG](https://github.com/mxpoliakov/Multi-Meta-RAG).  \nIndex Terms  \nretrieval-augmented generation, probing classifiers, metadata filtering, multi-hop question answering, small language models  \nI. INTRODUCTION  \nRetrieval-augmented generation (RAG) [1] grounds a language model on documents fetched from an external store, but struggles on multi-hop queries that must assemble evidence from several documents at once. The MultiHop-RAG benchmark [2] makes this failure mode concrete: 2556 news queries whose answers require combining two to four articles. Multi-Meta-RAG [3] improves multi-hop retrieval on this benchmark by filtering the vector store on metadata before similarity search, specifically the news source (e.g. The Verge, TechCrunch) named in the query. The filter is obtained by prompting gpt-3 .5-turbo to read the source(s) out of the query text.  \nThis extractor has two costs. First, it is a proprietary API call on every query, adding token cost and network latency to the retrieval path. Second, it drifts: despite a prompt that fixes a 49-source allow-list, GPT-3.5 emits 128 distinct source strings across the dataset, 79 of them off-list, so many of its filters reference labels the vector store never indexed.  \nOur starting observation is that the source named in a query is almost always present in the query surface form: the source name appears verbatim in the query in 95.4% of gold query–source pairs. Extraction therefore need not be a generative act. The information required is already linearly available in the model’s own internal representation. We therefore train a lightweight probe [4] on the hidden states of a small open-source model, motivated by evidence that intermediate representations carry task-relevant signal for retrieval [5] . The probe is a fixed-vocabulary multi-label classifier: its output space is exactly the 49 sources, so it is structurally incapable of the allow-list drift the prompted baseline exhibits.  \nOur main contributions are as follows.  \n1) A drop-in, fixed-vocabulary probe that replaces GPT-3.5 source extraction in Multi-Meta-RAG, with no API cost, noallow-list drift, and deterministic output.  \n2) A set-exact head-to-head on all 2556 MultiHop-RAG queries against both GPT-3.5 and a model-free substring baseline: the probe scores 90.9% overall versus 88.0%(substring) and 80.9%(GPT-3.5), with its margin coming entirely from null-query abstention; on non-null queries it stays within ab","cbCaiamvl8CfUTC0","https://ap.wps.com/l/cbCaiamvl8CfUTC0","pdf",354166,"English","# Abstract\n# Introduction\n## Multi-Meta-RAG and multi-hop retrieval challenges\n## Proposed observation and probe-based solution\n## Contributions and paper structure\n# Related Work\n## Probing classifiers\n## Which layer carries the signal\n## RAG and metadata filtering","[{\"question\":\"What problem does Multi-Meta-RAG address in multi-hop question answering?\",\"answer\":\"It tackles retrieval failures on multi-hop queries by filtering a vector store using news-source metadata so evidence from multiple documents can be assembled more reliably.\"},{\"question\":\"How does the paper replace GPT-3.5-based metadata extraction?\",\"answer\":\"It trains a local deterministic probe on hidden states of a small open-source language model to predict the source labels from a fixed 49-source vocabulary.\"},{\"question\":\"Why is the probe less prone to errors than the prompted GPT-3.5 extractor?\",\"answer\":\"Because the probe’s output space is exactly the allow-list of 49 sources, it cannot drift to off-list labels that the prompted model sometimes produces.\"}]","Probe, Don’t Prompt: A Hidden-State Probe for Metadata Filtering in Multi-Meta-RAG | PDF",1789978074,13]