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The role of sufficient context

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Retrieval augmented generation (RAG) enhances large language models (LLMs) by providing them with relevant external context. For example, when using a RAG system for a question-answer (QA) task, the LLM receives a context that may be a combination of information from multiple sources, such as public webpages, private document corpora, or knowledge graphs. Ideally, the LLM either produces the correct answer or responds with “I don’t know” if certain key information is lacking.

A main challenge with RAG systems is that they may mislead the user with hallucinated (and therefore incorrect) information. Another challenge is that most prior work only considers how relevant the context is to the user query. But we believe that the context’s relevance alone is the wrong thing to measure — we really want to know whether it provides enough information for the LLM to answer the question or not.

In “Sufficient Context: A New Lens on Retrieval Augmented Generation Systems”, which appeared at ICLR 2025, we study the idea of “sufficient context” in RAG systems. We show that it’s possible to know when an LLM has enough information to provide a correct answer to a question. We study the role that context (or lack thereof) plays in factual accuracy, and develop a way to quantify context sufficiency for LLMs. Our approach allows us to investigate the factors that influence the performance of RAG systems and to analyze when and why they succeed or fail.

Moreover, we have used these ideas to launch the LLM Re-Ranker in the Vertex AI RAG Engine. Our feature allows users to re-rank retrieved snippets based on their relevance to the query, leading to better retrieval metrics (e.g., nDCG) and better RAG system accuracy.



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Commanders vs. Packers props, SportsLine Machine Learning Model AI picks: Jordan Love Over 223.5 passing yards

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The NFL Week 2 schedule gets underway with a Thursday Night Football matchup between NFC playoff teams from a year ago. The Washington Commanders battle the Green Bay Packers beginning at 8:15 p.m. ET from Lambeau Field in Green Bay. Second-year quarterback Jayden Daniels led the Commanders to a 21-6 opening-day win over the New York Giants, completing 19 of 30 passes for 233 yards and one touchdown. Jordan Love, meanwhile, helped propel the Packers to a dominating 27-13 win over the Detroit Lions in Week 1. He completed 16 of 22 passes for 188 yards and two touchdowns. 

NFL prop bettors will likely target the two young quarterbacks with NFL prop picks, in addition to proven playmakers like Terry McLaurin, Deebo Samuel and Josh Jacobs. Green Bay’s Jayden Reed has been dealing with a foot injury, but still managed to haul in a touchdown pass in the opener. The Packers enter as a 3.5-point favorite with Green Bay at -187 on the money line. The over/under is 48.5 points. Before betting any Commanders vs. Packers props for Thursday Night Football, you need to see the Commanders vs. Packers prop predictions powered by SportsLine’s Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop. 

For Packers vs. Commanders NFL betting on Monday Night Football, the Machine Learning Model has evaluated the NFL player prop odds and provided Bears vs. Vikings prop picks. You can only see the Machine Learning Model player prop predictions for Washington vs. Green Bay here.

Top NFL player prop bets for Commanders vs. Packers

After analyzing the Commanders vs. Packers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model says Packers quarterback Love goes Over 223.5 passing yards (-112 at FanDuel). Love passed for 224 or more yards in eight games a year ago, despite an injury-filled season. In 15 regular-season games in 2024, he completed 63.1% of his passes for 3,389 yards and 25 touchdowns with 11 interceptions.

In a 30-13 win over the Seattle Seahawks on Dec. 15, he completed 20 of 27 passes for 229 yards and two touchdowns. Love completed 21 of 28 passes for 274 yards and two scores in a 30-17 victory over the Miami Dolphins on Nov. 28. The model projects Love to pass for 259.5 yards, giving this prop bet a 4.5 rating out of 5. See more NFL props here, and new users can also target the FanDuel promo code, which offers new users $300 in bonus bets if their first $5 bet wins:

How to make NFL player prop bets for Washington vs. Green Bay

In addition, the SportsLine Machine Learning Model says another star sails past his total and has nine additional NFL props that are rated four stars or better. You need to see the Machine Learning Model analysis before making any Commanders vs. Packers prop bets for Thursday Night Football.

Which Commanders vs. Packers prop bets should you target for Thursday Night Football? Visit SportsLine now to see the top Commanders vs. Packers props, all from the SportsLine Machine Learning Model.





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Research Solutions Launches AI Copyright Tool for Scientific Research

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Solution Enables Corporate Researchers To Safely Use Generative AI Tools With Journal Articles Through Integrated Rights Management And Publisher Partnerships

HENDERSON, Nev., Sept. 11, 2025 /PRNewswire/ — Research Solutions (NASDAQ: RSSS), a leading provider of AI-powered scientific research tools, announces the commercial launch of its AI Rights add-on for Article Galaxy, enabling corporate researchers to compliantly use generative AI tools with scientific journal content at scale. The solution addresses a critical compliance gap affecting 76% of researchers who now use AI tools in their workflows but lack clear guidance on copyright permissions for scientific content analysis.

The AI Rights add-on transforms Research Solutions’ Article Galaxy platform into a comprehensive solution for AI rights verification and acquisition, providing instant clarity on usage permissions and seamless access to acquire necessary rights. With direct partnerships with major publishers, the solution enables researchers to confidently analyze scientific literature with enterprise AI platforms like Microsoft Copilot, ChatGPT, and Claude while maintaining full copyright compliance.

“Our customers have been clear: they need AI capabilities to accelerate their research, but they cannot risk non-compliance,” said Roy W. Olivier, CEO of Research Solutions. “This launch delivers on our commitment to eliminate friction in the research workflow while creating sustainable value for publishers. We’re solving a compliance problem while enabling a new era of AI-powered scientific research.”

Research teams confront several complex obstacles when attempting to integrate AI tools into their workflows. Most publishers explicitly prohibit the use of their content in AI applications, yet no streamlined mechanism exists for acquiring necessary permissions. Research Solutions’ AI Rights add-on solves this through several key innovations:

  • Comprehensive Rights Management: Users can manage all AI rights sources through a single interface—whether through open access licenses, Reproduction Rights Organization agreements (RROs), direct publisher relationships, or Article Galaxy marketplace acquisition
  • Instant Rights Verification: Users immediately see AI usage permissions for any article, removing guesswork and compliance uncertainty
  • One-Click Rights Acquisition: Missing permissions can be purchased directly through the Article Galaxy interface with transparent pricing from participating publishers
  • Retroactive Rights Purchase: Organizations can acquire AI rights for articles previously purchased, enabling immediate compliance for existing content libraries
  • Organization-Wide Licensing: AI Rights acquired apply across the entire organization, eliminating per-use restrictions and ongoing compliance concerns

“The combination of generative AI and scientific literature creates unprecedented opportunities for accelerating discovery, but only when researchers can access content legally and efficiently,” said Chris Bendall, VP of Product Strategy at Research Solutions. “We’ve built a solution that makes AI analysis of scientific content both legally compliant and operationally seamless—turning what was previously a compliance risk into a competitive advantage.”

About Research Solutions

Research Solutions (NASDAQ: RSSS) is a vertical SaaS and AI company that simplifies research workflow for academic institutions, life science companies, and research organizations worldwide. As one of the only publisher-independent marketplaces for scientific, technical, and medical (STM) content, the company uniquely combines AI-powered tools—including an intelligent research assistant and full-text search capabilities—with seamless access to both open access and paywalled research. The platform enables organizations to discover, access, manage, and analyze scientific literature more efficiently, accelerating the pace of scientific discovery.

SOURCE Research Solutions, Inc. | LinkedIn | Facebook | X 

For more information, visit https://www.researchsolutions.com.

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SOURCE Research Solutions, Inc.





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BNY and Carnegie Mellon University Join Forces to Advance Research and Development in AI

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BNY and Carnegie Mellon University (CMU) announced a five-year, $10 million agreement to support world-class research and development in artificial intelligence (AI). The collaboration will bring students, faculty and staff from across the University together with BNY experts to advance the in AI applications and systems and prepare the next generation of leaders. The research collaboration, known as the BNY AI Lab, will focus on developing technologies and frameworks that can ensure the robust governance, trust and accountability required to deploy mission-critical AI applications, including those powering financial services.

Drawing on the expertise of Carnegie Mellon’s top-ranked programs in computer science, AI and business, the lab will work with BNY to advance both theoretical and applied AI. Additionally, BNY will support cross-disciplinary courses and talent recruitment across all CMU schools and colleges. As part of the agreement, a dedicated space will be created on CMU’s Pittsburgh campus during the 2025-26 academic year.

This space will support the full scope of the collaboration–including joint research, education projects, and talent recruitment–and will also provide opportunities for BNY employees to work directly with CMU students and faculty. Once in motion, the alliance will aim to realize progress on a number of fronts, including: strengthening Pittsburgh’s position as a global hub for the advancement of AI, fostering economic development across Western Pennsylvania and directly supporting CMU’s efforts to train the next generation of AI and data leaders and drivers. This latest development underscores the long-term relationship between CMU and BNY over many years, highlighted by BNY’s position as the larger employer of graduates from CMU’s Master of Science in Artificial Intelligence and Innovation (MSAII) program.



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