ICNS 2025 Abstract Deadline Extended to 1 December 2024 https://lnkd.in/gKHYD-WH Due to the number of requests for extra time, the abstract submission deadline date has been extended. If you have important knowledge and research to share, the original abstract deadline should no longer be a barrier.
ICNS Conference’s Post
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#UPCdatabase #decisionoftheweek CoA, March 21, 2024, order, UPC_CoA_486/2023 #luxembourgcourtofappeal #noticepursuanttor202rop #appealabledecision #applicationforreviewofcasemanagementdecision #casemanagementdecision #discretionaryreview #judgerapporteur #preliminaryobjection #r220rop #r2203rop #r2212rop #r333rop #requestfordiscretionaryreviewpursuanttor2203rop #reviewofcasemanagementdecision ! Key takeaways: An appeal against a decision of the judge rapporteur can be admissible if it is the subject of the appeal whether the judge rapporteur was entitled to issue the decision under appeal.: R 220.3 RoP provides for the admission of an appeal against an order issued by a panel. However, the order under appeal was ...
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My thanks to Charlotte Hindle for considering mine and Emma Hague’s case law overview sufficiently interesting to publish. You can access via the below link with our article located at page 83.
We are pleased to announce the latest issue of our Expert Witness Journal is available to view, please see link below. If you would like a printed copy, please get in touch. #expertwitness https://lnkd.in/e4NMxnRn
ISSUE 54 EWJ web - Journal - Page 1
paperturn-view.com
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FINAL REMINDER: SUBMIT YOUR ABSTRACT NOW! A gentle reminder that the submission deadline is 30 April 2024, 23.59 GMT +1 Submit your abstract now before the deadline and click on the link https://lnkd.in/eFbNH2YU for more information about abstract submission!
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Senior Associate in Professional and Commercial Risk at DAC Beachcroft LLP specialising in lawyers’ liability
🖋️ Delighted to have contributed to the recent Expert Witness Journal ‘Negligence’ edition. You can find my article with some top tips for solicitors navigating their regulatory requirements and their duties to their clients on page 2.
ISSUE 54 EWJ web - Journal - Page 1
paperturn-view.com
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The latest issue of Expert Witness is now available online! If you get 10 spare minutes in your day, I would thoroughly recommend reading this hugely insightful article on pages 23-24 by the fantastic Michelle Caton-Richards, Deputy Quality Assurance & Training Lead from Somek & Associates; "Do Nurses & Midwives have adequate knowledge and understanding of medical negligence and the medico-legal process?" #expertwitness #nursing #midwifery #clinicalnegligence
We are pleased to announce the latest issue of our Expert Witness Journal is available to view, please see link below. If you would like a printed copy, please get in touch. #expertwitness https://lnkd.in/e4NMxnRn
ISSUE 54 EWJ web - Journal - Page 1
paperturn-view.com
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Senior Software Engineer | Cloud -AWS & Azure | Gen AI, RAG, LLMs, Agent | Microservice, RESTful APIs | Dynamics 365 F&O, Field Service & PowerApps
A inspector or reviewer is always welcome who can tell what can be improved or what is not correct here it's goes CRAG to facilitate it
A cool trick for improving retrieval quality for RAG is to include retrieval-evals in-the-loop. Given a set of retrieved results, use an LLM evaluator to decide how relevant each context is to the query, before synthesizing an answer. You can use this to filter results, augment context from “backup” sources, and more. This core idea was proposed in the recent CRAG paper (Corrective Retrieval Augmented Generation) by Yan et al., and now it’s available as a LlamaPack thanks to Ravi Theja Desetty! Check it out 👇 https://lnkd.in/gwj6_zDW Paper: https://lnkd.in/gtbwiE2C
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Retrieved chunk correction is an interesting idea to try out within a RAG pipeline.
A cool trick for improving retrieval quality for RAG is to include retrieval-evals in-the-loop. Given a set of retrieved results, use an LLM evaluator to decide how relevant each context is to the query, before synthesizing an answer. You can use this to filter results, augment context from “backup” sources, and more. This core idea was proposed in the recent CRAG paper (Corrective Retrieval Augmented Generation) by Yan et al., and now it’s available as a LlamaPack thanks to Ravi Theja Desetty! Check it out 👇 https://lnkd.in/gwj6_zDW Paper: https://lnkd.in/gtbwiE2C
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Founder and Executive Director, Asthma Hub LLC. Certified Health Education Specialist (CHES). Certified Asthma Educator (AE-C). Certified Patient Leader. Creator of My Life as an Asthma Mom
Hey locals, do you have research to share? 👩💻 Want to brag about results from your fabulous program? 📈 Utah Society of Public Health Education abstracts are due August 15th......you have 2 days to submit your abstract! See you in November! #PublicHealth #CertifiedHealthEducationSpecialist #CHES #HealthEducation #HealthEducator #Research #Programs #AsthmaEducator
Abstract submissions are due in one week! Don't forget to submit yours! For more information and to submit go to: https://bit.ly/3ylbdWl
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A cool trick for improving retrieval quality for RAG is to include retrieval-evals in-the-loop. Given a set of retrieved results, use an LLM evaluator to decide how relevant each context is to the query, before synthesizing an answer. You can use this to filter results, augment context from “backup” sources, and more. This core idea was proposed in the recent CRAG paper (Corrective Retrieval Augmented Generation) by Yan et al., and now it’s available as a LlamaPack thanks to Ravi Theja Desetty! Check it out 👇 https://lnkd.in/gwj6_zDW Paper: https://lnkd.in/gtbwiE2C
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Generative AI Expert | Technology | Innovation | Corporate Strategy | New Ventures | 50+ Patents inventor | xBCG
Interesting article about further improving context retrieval for RAG applications. It introduces additional steps that distill and disambiguate retrieved data through reasoning and additional search. Worth a read! HeyIris.AI
A cool trick for improving retrieval quality for RAG is to include retrieval-evals in-the-loop. Given a set of retrieved results, use an LLM evaluator to decide how relevant each context is to the query, before synthesizing an answer. You can use this to filter results, augment context from “backup” sources, and more. This core idea was proposed in the recent CRAG paper (Corrective Retrieval Augmented Generation) by Yan et al., and now it’s available as a LlamaPack thanks to Ravi Theja Desetty! Check it out 👇 https://lnkd.in/gwj6_zDW Paper: https://lnkd.in/gtbwiE2C
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