Evaluating Output Sequences Using an Auto-Regressive Language Model
By NizamUdDeen · · Reviewed by the Nizam SEO War Room editorial team.
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What is Evaluating Output Sequences Using an Auto-Regressive Language Model?
Patent overview Inventor Noam Shazeer, others Assignee Google LLC Patent number US 12,086,713 Filing or grant year September 10, 2024 Patent family llm-evaluation Track Noam Shazeer, Google Transforme
Patent overview Inventor Noam Shazeer, others Assignee Google LLC Patent number US 12,086,713 Filing or grant year September 10, 2024 Patent family llm-evaluation Track Noam Shazeer, Google Transforme
NizamUdDeen, Nizam SEO War Room
Patent overview
Inventor
Noam Shazeer, others
Assignee
Google LLC
Patent number
US 12,086,713
Filing or grant year
September 10, 2024
Patent family
llm-evaluation
Track
Noam Shazeer, Google Transformer, MoE & Search Patents
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What this patent covers
~46 captured Google patents (full portfolio estimated at 120-150) by Noam Shazeer. Co-inventor on the foundational Transformer attention architecture (US 10,452,978 with Vaswani, Polosukhin, Uszkoreit, Jones, Gomez, Kaiser, Parmar), the Sparsely-Gated Mixture-of-Experts scaling approach (US 11,769,055 with Dean, Hinton, Le, Mirhoseini), the Switch Transformer (US 12,093,829), and the Navboost implicit-feedback ranking family (US 8,661,029 with Kim/Tong/Diligenti — cross-listed). Also covers LLM-in-assistant response generation, distributed tensor computations (GSPMD/Pathways), and the 2008-2015 pre-Transformer large-scale ML ranking infrastructure. Spans 2008 to 2025.
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Why Evaluating Output Sequences Using an Auto-Regressive Language Model matters
This patent is part of the Noam Shazeer, Google Transformer, MoE & Search Patents research track inside the Nizam SEO War Room patents archive. It describes a piece of the search-engine machinery that working SEOs need to understand to optimize against modern ranking and retrieval systems. A deeper annotated walkthrough of this patent — covering the claims, the disclosure, the prior art it cites, and the algorithms it influences — is queued for the next archive expansion pass.
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Related research
Patents in the Noam Shazeer, Google Transformer, MoE & Search Patents track are cross-linked to neighboring tracks where the same inventor or research lineage continues. Read this patent alongside the other entries in the track to recover the full research arc — the original disclosure, its continuations and divisional applications, and any follow-up patents that branched from the same line of work.
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For example, a working SEO consultant uses Evaluating Output Sequences Using an Auto-Regressive Language Model when diagnosing a ranking drop, planning a content calendar, or briefing a client on why a tactic shifted. However, the concept only compounds when paired with the surrounding entries in the encyclopedia and patents archive. In addition, the platform connects this concept to live SERP data so the theory carries through to execution.
How does Evaluating Output Sequences Using an Auto-Regressive Language Model work in modern search?
The full breakdown is in the article body above. In short: Evaluating Output Sequences Using an Auto-Regressive Language Model ties into how search engines and AI answer engines weigh signals — every detail (definition, ranking impact, related patents, related signals) is captured in this article and cross-linked to neighboring entries in the encyclopedia and patents archive.
Working SEOs reach for Evaluating Output Sequences Using an Auto-Regressive Language Model when diagnosing why a page ranks where it does, when planning a content strategy that aligns with the surfaces search engines and answer engines weigh, and when explaining ranking moves to non-technical stakeholders. The concept is one piece of the broader Semantic SEO + AEO operating system; the Nizam SEO War Room platform ties it to live SERP data, the patent lineage that introduced it, and the strategy moves that compound across projects.
Where Evaluating Output Sequences Using an Auto-Regressive Language Model fits in the Semantic SEO + AEO stack
Search engines have moved from keyword matching toward semantic understanding, entity reasoning, and AI-mediated answer generation. Evaluating Output Sequences Using an Auto-Regressive Language Model sits inside that shift — its weight, its measurement, and its downstream effects all changed when the underlying ranking and retrieval systems changed. Read the related encyclopedia entries linked above for the surrounding context.
The concept of Evaluating Output Sequences Using an Auto-Regressive Language Model is grounded in the search-engine research lineage tracked in the Nizam SEO War Room platform. Primary sources:
Related encyclopedia entries and patent walkthroughs are linked inline above. The Strategy Brain inside the platform connects these sources to live project state so the research has a direct execution surface.
Finally, to summarize. Evaluating Output Sequences Using an Auto-Regressive Language Model matters because it intersects directly with the signals search engines and AI answer engines use to rank and surface results. The full article above covers the mechanism in depth, the patents it derives from, and the related encyclopedia entries to read next.