{"id":9863,"date":"2026-10-07T10:00:00","date_gmt":"2026-10-07T10:00:00","guid":{"rendered":"https:\/\/dev95.site\/can-you-read-two-million-pages-an-llm-can-help\/"},"modified":"2026-10-07T10:00:00","modified_gmt":"2026-10-07T10:00:00","slug":"can-you-read-two-million-pages-an-llm-can-help","status":"publish","type":"post","link":"https:\/\/dev95.site\/ar\/can-you-read-two-million-pages-an-llm-can-help\/","title":{"rendered":"Can You Read Two Million Pages? An LLM Can Help."},"content":{"rendered":"<div id=\"dev95-2927230078\" class=\"dev95-- dev95-entity-placement\"><script async=\"async\" data-cfasync=\"false\" src=\"https:\/\/pl27862732.profitableratecpmnetwork.com\/2ad7a50e0bbc23ac6801d7b77c501463\/invoke.js\"><\/script>\r\n<div id=\"container-2ad7a50e0bbc23ac6801d7b77c501463\"><\/div><\/div><div id=\"dev95-118989942\" class=\"dev95-before-content dev95-entity-placement\"><p style=\"text-align: center;\"><strong>Stop wasting time on dead links! \ud83d\uded1 This smart platform automatically detects your device and country to give you the exact best offer instantly. Check it out now 100% free! \ud83d\udc47<\/strong><br data-sfc-root=\"ep\" data-sfc-pl=\"|||[]\" data-complete=\"true\" data-copy-service-computed-style=\"font-family: Arial, sans-serif, &quot;Noto Color Emoji&quot;; font-size: 16px; font-weight: 400; margin: 0px; text-decoration: none; border-bottom: 0px rgb(10, 10, 10);\" \/><a href=\"https:\/\/www.profitableratecpmnetwork.com\/pvhx5mbcc?key=ff7d362db3c20bc86fad685cc94f1fc4\">\ud83d\udd17 <strong class=\"rQesXe MPyX\" data-sfc-cp=\"\" data-sfc-root=\"ep\" data-complete=\"true\" aria-owns=\"action-menu-parent-container\" data-copy-service-computed-style=\"font-family: Arial, sans-serif, &quot;Noto Color Emoji&quot;; font-size: 16px; font-weight: 700; margin: 0px; text-decoration: none; border-bottom: 0px rgb(10, 10, 10);\">[Click here]<\/strong><\/a><\/p>\n<\/div><div>\n<p class=\"wp-block-paragraph\"><em>Jacob Polay with Chlo\u00eb\u00a0Farr and Jessica Jack<\/em><\/p><div id=\"dev95-1121830018\" class=\"dev95- dev95-entity-placement\"><center>\r\n<script>\r\n  atOptions = {\r\n    'key' : '4ba6b6513c00e0ba76511f798ae56401',\r\n    'format' : 'iframe',\r\n    'height' : 50,\r\n    'width' : 320,\r\n    'params' : {}\r\n  };\r\n<\/script>\r\n<script src=\"https:\/\/www.highrevenueformat.com\/4ba6b6513c00e0ba76511f798ae56401\/invoke.js\"><\/script>\r\n\t<\/center><\/div>\n<p class=\"wp-block-paragraph\"><em>This post is part of a <a href=\"https:\/\/activehistory.ca\/ai-and-collaboration\/\">series on AI and Collaboration.<\/a><\/em><\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"809\" data-attachment-id=\"144104\" data-permalink=\"https:\/\/activehistory.ca\/ibm_card_storage-nara\/\" data-orig-file=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_.jpg\" data-orig-size=\"1920,1516\" data-comments-opened=\"1\" data-image-title=\"IBM_card_storage.NARA\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Storage of IBM record cards at the Federal records center in Alexandria, Virginia, November 1959. US NARA 64-NA-1715. Public domain.&lt;\/p&gt;\n\" data-large-file=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-1024x809.jpg\" src=\"https:\/\/i0.wp.com\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-1024x809.jpg?resize=1024%2C809&#038;ssl=1\" alt=\"Black and white photo of hundreds of stacked, labeled boxes, viewed from above.\" class=\"wp-image-144104\" srcset=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-1024x809.jpg 1024w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-300x237.jpg 300w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-768x606.jpg 768w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-1536x1213.jpg 1536w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_-624x493.jpg 624w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/IBM_card_storage.NARA_.jpg 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\"><figcaption class=\"wp-element-caption\"><em>Storage of IBM record cards at the Federal records center in Alexandria, Virginia, November 1959. US NARA 64-NA-1715. Public domain.<\/em><\/figcaption><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">Researchers often face a dilemma when working with digital archives. Once they establish their research questions, they scour the web for digitized archival material, often finding thousands of sources. Next they are faced with the daunting task of turning this diverse array of data, composed of tables, ledgers, letters, diaries, bills, and government acts, into one set of relationships that can answer historical questions. Algorithmic transcription [OCR HYPERLINK] and entity tagging [NER HYPERLINK] help solve the first two steps of creating these relationships. These methods allow the computer to read the sources and organize their information into relevant entity groups to answer the researcher\u2019s questions.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">However, these helpful technologies still leave one crucial hurdle that must be overcome. A corpus of 20,000 documents is still 20,000 disconnected files. A search bar can only find specific words or phrases. Context is missing. It cannot answer the questions historians actually ask about the relationships between actor and entity: who traded with whom, who went where, what replaced what, and what came from where. Answering these questions at this large archival scale is much easier and faster with machines, but requires teaching the archive to hold its knowledge the way historians do. Three connected technologies now make that possible, made even more accessible with the use of an LLM.<\/p>\n<p class=\"wp-block-paragraph\">The first technology is one that humanists have been building toward for decades. Knowledge graphs store information not as tables but as relationships: entities become nodes, and the connections between them become typed edges: sugar GROWN_IN Jamaica, sugar HARVESTED_BY enslaved Africans, sugar EXPORTED_TO London. This technology may sound familiar. It\u2019s the same structure powering software like Uber, AirBnB, and eBay, and is also the foundation of Linked Open Data which is the basis for Wikidata, Geonames, the LUX: Yale Collections, and others. These open sources publish entity-relationship data with shared identifiers so that the historical spelling \u201cBarbadoes\u201d in one online collection resolves to the same island as \u201cBarbados\u201d in others. A knowledge graph is essentially this idea at the scale of a single project: a map of every documented relationship in your corpus, queryable in milliseconds.<\/p>\n<p><span id=\"more-144105\"><\/span><\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"349\" data-attachment-id=\"144102\" data-permalink=\"https:\/\/activehistory.ca\/kg-storage\/\" data-orig-file=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/kg-storage.png\" data-orig-size=\"512,349\" data-comments-opened=\"1\" data-image-title=\"kg storage\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Figure 1: Simplified Knowledge Graph Visualization&lt;br \/&gt;\nCreated by Jacob Polay with assistance from Claude Fable 5.&lt;\/p&gt;\n\" data-large-file=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/kg-storage.png\" src=\"https:\/\/i0.wp.com\/activehistory.ca\/wp-content\/uploads\/2026\/09\/kg-storage.png?resize=512%2C349&#038;ssl=1\" alt=\"Diagram of how a knowledge graph correlates concepts, people, and directions.\" class=\"wp-image-144102\" srcset=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/kg-storage.png 512w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/kg-storage-300x204.png 300w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\"><figcaption class=\"wp-element-caption\"><em>Simplified Knowledge Graph Visualization. Created by Jacob Polay with assistance from Claude Fable 5.<\/em><\/figcaption><\/figure>\n<\/div>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"512\" height=\"244\" data-attachment-id=\"144103\" data-permalink=\"https:\/\/activehistory.ca\/map-meaning\/\" data-orig-file=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/map-meaning.png\" data-orig-size=\"512,244\" data-comments-opened=\"1\" data-image-title=\"map meaning\" data-image-description=\"\" data-image-caption=\"&lt;p&gt;Simplified Vector Retrieval Pipeline. Created by Jacob Polay with assistance from Claude Fable 5.&lt;\/p&gt;\n\" data-large-file=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/map-meaning.png\" src=\"https:\/\/i0.wp.com\/activehistory.ca\/wp-content\/uploads\/2026\/09\/map-meaning.png?resize=512%2C244&#038;ssl=1\" alt='Title: \"From text to a map of meaning. An embedding model turns each chunk into coordinates; similar meanings land close together.\" A diagram representing how a passage of text is mapped in relation to similar topics.' class=\"wp-image-144103\" srcset=\"https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/map-meaning.png 512w, https:\/\/activehistory.ca\/wp-content\/uploads\/2026\/09\/map-meaning-300x143.png 300w\" sizes=\"auto, (max-width: 512px) 100vw, 512px\"><figcaption class=\"wp-element-caption\"><em>Simplified Vector Retrieval Pipeline. Created by Jacob Polay with assistance from Claude Fable 5.<\/em><\/figcaption><\/figure>\n<\/div>\n<p class=\"wp-block-paragraph\">The second technology, Retrieval-Augmented Generation (RAG) is the technology behind every \u201cchat with your documents\u201d product, like Google\u2019s NotebookLM. To prepare a corpus, algorithms split the text of your documents into \u201cchunks\u201d every few hundred words. Each chunk then passes through an embedding model, which converts it into a vector, a long string of numbers that acts like coordinates on a map of meaning. Trained on enormous amounts of text, embedding models place passages with similar meanings at nearby coordinates, so chunks about sugar, cane fields, and muscovado all cluster in the same neighbourhood. When you query a RAG system, your question is also converted into a vector, and the system retrieves whichever chunks sit geometrically closest to it. Once selected, it hands those chunks to an LLM to summarize. Importantly, the LLM draws only on the corpus you assign it and is instructed to answer from and cite only those retrieved chunks. These citations make RAG far more trustworthy than a chatbot answering from memory. In practice, however, RAG fails for history as geometry on a map produces only what is most similar to your question, not what will actually answer it. For example, when asked about sugar exports, RAG quickly returned five lyrical descriptions of sugar cane from the same poetry book, while crucial evidence in customs ledgers sat unretrieved.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">GraphRAG, the third technology, combines the previous two: the knowledge graph directs the search, following edges from the entities in your question to the passages that actually mention them, before mathematical similarity narrows the results. The knowledge graph decides where to look, and the RAG decides what to read, leading to more targeted, accurate, and helpful responses.<\/p>\n<p class=\"wp-block-paragraph\">In my MA thesis, I built one example of how GraphRAG can work for historians. From 20,000 early modern documents, I constructed a knowledge graph of 218,000 entities and 692,000 relationships, then tested eight retrieval systems on thirty historical questions of escalating difficulty and then grading each of them. Plain RAG scored 48.4 percent against the metrics of groundedness, completeness, accuracy, synthesis, and usefulness. The best off-the-shelf system reached only 61.6 percent, an unacceptable result for a careful historian.\u00a0<\/p>\n<p class=\"wp-block-paragraph\">The best performing of all, at 71.2 percent, was an architecture I designed to imitate how historians read. After significant research on my part into the appropriate architecture and approach to solving this problem, I turned to an LLM to facilitate the implementation of my chosen design. Through the help of a coding sub-agent with the LLM, I created a system that breaks a question into sub-questions, uses GraphRAG to find sources and take notes on each source separately, flags contradictions between sources instead of averaging them into false consensus, and queries the knowledge graph directly when a question needs numbers. Asked how Caribbean ginger exports changed over sixty years, this \u201chistorical-thinking\u201d system parsed through the records and queried the knowledge graph to return a sourced, decade-by-decade answer no similarity-based system could produce because no single page of prose contained a sixty-year summary. The answer existed only in the relationships formed in the aggregate: exports surged between 1706-1708, had a mid-century revival between 1733-1735, and then faced a precipitous drop after 1744, falling to under 90% of the 1735 peak.<\/p>\n<p class=\"wp-block-paragraph\">While the knowledge graph above was built for a single project by a single researcher, the future of GraphRAG in history should not be solitary. Knowledge Graphs are collaborative by design. Their application in Linked Open Data repositories means that a \u201cBarbados\u201d from my project could link to a \u201cBarbadoes\u201d in yours. Imagine, for example, several graphs built for different projects but sharing temporality, geography, or theme\u2014Caribbean slavery, English criminal courts, North American colony registers\u2014resolving the same people, places, organizations, and commodities. In the future, historians could use this interoperable data within a GraphRAG system so that one query surfaces evidence from all graphs. Getting to this point requires historians of every specialty contributing: creating manual gold-standard documents, ontologies, test questions, and blind grading of the GraphRAG systems.The pipeline I built was simply a prototype of what could happen through collaboration. The archive is too big to read alone.<\/p>\n<p class=\"wp-block-paragraph\"><em><strong>Jacob Polay<\/strong>\u00a0is a PhD student in History at the University of Saskatchewan, studying the roles Large Language Models have in the historical method. His current research involves creating an information retrieval pipeline using artificial intelligence tools to unlock the early modern archive at scale.<\/em><\/p>\n<p class=\"wp-block-paragraph\"><strong><em>Chlo\u00eb Farr<\/em><\/strong><em>\u00a0is a researcher working at the intersection of artificial intelligence, archives, and digital humanities.<\/em> <em>Working out of the Open Science Lab at TIB \u2013 Leibniz Information Centre for Science and Technology, her<\/em> <em>research focuses on large-scale text recognition and analysis of historical documents, including newspapers, maps, and archival records. Learn more about Farr\u2019s work on<\/em> <a href=\"https:\/\/chloe-farr.github.io\/\"><em>GitHub<\/em><\/a><em>.<\/em><\/p>\n<p class=\"wp-block-paragraph\"><em><strong>Jessica Jack<\/strong>\u00a0is a PhD student in History at the University of Saskatchewan, developing applications for Large Language Models in historical research. They are doing so through studying settler land use in late 19th century and early 20th century Saskatchewan.<\/em><\/p>\n<\/div>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_9863\" class=\"pvc_stats total_only  \" data-element-id=\"9863\" style=\"\"><i class=\"pvc-stats-icon large\" aria-hidden=\"true\"><svg aria-hidden=\"true\" focusable=\"false\" data-prefix=\"far\" data-icon=\"chart-bar\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\" class=\"svg-inline--fa fa-chart-bar fa-w-16 fa-2x\"><path fill=\"currentColor\" d=\"M396.8 352h22.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-192 0h22.4c6.4 0 12.8-6.4 12.8-12.8V140.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h22.4c6.4 0 12.8-6.4 12.8-12.8V204.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zM496 400H48V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-16c0-8.84-7.16-16-16-16zm-387.2-48h22.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8z\" class=\"\"><\/path><\/svg><\/i> <img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/i0.wp.com\/dev95.site\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif?resize=16%2C16&#038;ssl=1\" border=0 \/><\/p>\n<div class=\"pvc_clear\"><\/div>\n<div id=\"dev95-2071904864\" class=\"dev95-after-content dev95-entity-placement\"><p style=\"text-align: center;\"><strong>Finally, a link that actually works for your region and device! \ud83c\udf0d Get instant access to the top exclusive offers tailored just for you right now. Don&#8217;t miss out, click here! \ud83d\udc47<\/strong><br data-sfc-root=\"ep\" data-sfc-pl=\"|||[]\" data-complete=\"true\" data-copy-service-computed-style=\"font-family: Arial, sans-serif, &quot;Noto Color Emoji&quot;; font-size: 16px; font-weight: 400; margin: 0px; text-decoration: none; border-bottom: 0px rgb(10, 10, 10);\" \/><a href=\"https:\/\/www.profitableratecpmnetwork.com\/uzeja8ahze?key=bc876be53d6ad0ff6370ab8ea030e479\"><strong>\ud83d\udd17 [Click here]<\/strong><\/a><\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Jacob Polay with Chlo\u00eb\u00a0Farr and Jessica Jack This post is part of a series on AI and Collaboration. Storage of IBM record cards at the Federal records center in Alexandria, Virginia, November 1959. US NARA 64-NA-1715. Public domain. Researchers often<\/p>\n<div class=\"hosteria-entry-more\"><a href=\"https:\/\/dev95.site\/ar\/can-you-read-two-million-pages-an-llm-can-help\/\" class=\"no-underline font-light  group-hover:text-primary-800 dark:group-hover:text-primary-300 py-1\">Read more &gt;&gt;&gt;<\/a><\/div>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_9863\" class=\"pvc_stats total_only\" data-element-id=\"9863\" style=\"\"><i class=\"pvc-stats-icon large\" aria-hidden=\"true\"><svg aria-hidden=\"true\" focusable=\"false\" data-prefix=\"far\" data-icon=\"chart-bar\" role=\"img\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewbox=\"0 0 512 512\" class=\"svg-inline--fa fa-chart-bar fa-w-16 fa-2x\"><path fill=\"currentColor\" d=\"M396.8 352h22.4c6.4 0 12.8-6.4 12.8-12.8V108.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v230.4c0 6.4 6.4 12.8 12.8 12.8zm-192 0h22.4c6.4 0 12.8-6.4 12.8-12.8V140.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v198.4c0 6.4 6.4 12.8 12.8 12.8zm96 0h22.4c6.4 0 12.8-6.4 12.8-12.8V204.8c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v134.4c0 6.4 6.4 12.8 12.8 12.8zM496 400H48V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-16c0-8.84-7.16-16-16-16zm-387.2-48h22.4c6.4 0 12.8-6.4 12.8-12.8v-70.4c0-6.4-6.4-12.8-12.8-12.8h-22.4c-6.4 0-12.8 6.4-12.8 12.8v70.4c0 6.4 6.4 12.8 12.8 12.8z\" class=\"\"><\/path><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/dev95.site\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=\"0\" \/><\/p>\n<div class=\"pvc_clear\"><\/div>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fp_fajr_begins":"","fp_fajr_iqamah":"","fp_dhuhr_begins":"","fp_dhuhr_iqamah":"","fp_asr_begins":"","fp_asr_iqamah":"","fp_maghrib_begins":"","fp_maghrib_iqamah":"","fp_isha_begins":"","fp_isha_iqamah":"","fp_midnight":"","fp_midnight_name":"","fp_sunrise":"","fp_single_prayer_begins_title":"","fp_single_prayer_iqamah_title":"","fp_prayer_times_for_today":"","fp_hijra_date":"","fp_fajr_name":"","fp_dhuhr_name":"","fp_asr_name":"","fp_maghrib_name":"","fp_isha_name":"","fp_sunrise_name":"","fp_currentDate":"","fp_current_time":"","fp_current_title":"","fp_current_location":"","fp_masjid_name":"","fp_prayer_title":"","fp_next_prayer_iqamah_time":"","fp_next_prayer_iqamah_title":"","fp_next_prayer_begins_time":"","fp_next_prayer_begins_title":"","fp_next_prayer_title":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[37],"tags":[],"class_list":["post-9863","post","type-post","status-publish","format-standard","hentry","category-posts"],"a3_pvc":{"activated":true,"total_views":1,"today_views":1},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Can You Read Two Million Pages? 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