💻 Office Hour: 24th April, Wednesday, 16:00 CET

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💻 Office Hour: 24th April, Wednesday, 16:00 CET

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💻 Office Hour: 24th April, Wednesday, 16:00 CET

Hello all,

We invite you to join the Office Hour for the Amazon KDD Cup 2024. This session provides an opportunity to interact with the organizers, delve deep into the challenge details, and have your questions addressed directly by the organisers.

24th April, Wednesday, 16:00 CET
Join the Office Hour on Zoom

For those unable to attend, we will share a recording of the office hours. Feel free to post your questions here, and we will address them during office hours.

Office Hour Highlights:

• Direct engagement with the organizer
• Live Q&A session
• Share your feedback

Meet the Speaker:

Yilun Jin: PhD student at the Hong Kong University of Science and Technology and former intern at the Amazon Rufus team. Yilun is the main curator of the ShopBench dataset and has conducted extensive experiments on it.

If you can’t attend, leave your questions in the comments, and the organizers will address them during the session.

Mark your calendars, prepare your questions, and join the live Office Hour.

Looking forward to seeing you there!
Team Amazon KDD Cup 2024

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🛍️ Welcome to KDD Cup: 2024 Multi-Task Online Shopping Challenge for LLMS

Are you tired of the endless search for the perfect gift online? It’s like navigating a maze of products, reviews, and prices, only to feel overwhelmed by too many choices.

Introducing the Amazon Multi-Task Online Shopping Challenge, where we’re revolutionizing online shopping using Large Language Models (LLMs). Traditional methods miss the mark in understanding the nuance of shopping terminology, consumer behavior, and the wide array of products and languages, leaving users drowning in information.

Our ShopBench benchmark mirrors real-world shopping complexities, aiming to make online shopping as intuitive as having a knowledgeable assistant by your side. Participate to develop LLMs that can simplify shopping, making it a more intuitive and satisfying experience, much like a knowledgeable shopping assistant would in real life.

Multi-Task Online Shopping Challenge for LLMs

With 57 tasks and over 20,000 questions based on real Amazon data, this challenge pushes LLMs to excel in understanding shopping concepts, customer behavior, and multilingual support. Whether you’re a seasoned developer or a student, from the industry or academia, this challenge offers a platform to craft innovative LLM solutions that reshape online shopping experiences and valuable insights that benefit the whole community.

ShopBench, a comprehensive benchmark that mimics these real-world online shopping complexities, focuses on four main key shopping skills (which will serve as Tracks 1-4):

• shopping concept understanding
• shopping knowledge reasoning
• user behavior alignment
• multi-lingual abilities

Additionally, Track 5: All-around, promotes comprehensive solutions that address all tasks in Tracks 1-4 with a single, unified approach, offering larger rewards for these versatile solutions.

This challenge aims to give participants practical experience in crafting advanced LLM solutions for real issues, benefiting both the online service industry with robust, ready-to-implement LLM solutions and the wider machine learning community with valuable insights and training guidance.

Exciting Prizes

The challenge offers a total prize pool of $41,500, divided into three categories:

• Winner Prizes: Cash awards for the top three positions in each track.
• AWS Credits: Awarded to teams ranking immediately after the top three in each track.
• Student Awards: Special awards for the best student teams to support the development of resource-efficient LLM solutions due to the high computational costs and engineering efforts involved.

Prizes for Tracks 1-4:

• First place: $2,000
• Second place: $1,000
• Third place: $500
• 4th-7th places receive AWS Credit of $500
• Student Award: $750

Prizes for Track 5 (All-around):

• First place: $7,000
• Second place: $3,500
• Third place: $1,500
• 4th-8th places receive AWS Credit of $500
• Student Award: $2,000

Winners have the opportunity to present their work at the KDD Cup workshop 2024, held at ACM SIGKDD 2024 (August 2024, Barcelona, Spain).

Challenge Timeline

• Phase 1 Start Date: 21th March, 2024 23:55 UTC
• Entry Freeze Deadline and Phase 1 End Date: 10th May, 2024 23:55 UTC
• Phase 2 Start Date: 15th May, 2024 23:55 UTC
• End Date: 10th July, 2024 23:55 UTC
• Winner Notification: 15th July, 2024
• Winner Announcement: 26th August, 2024 (At KDD 2024)

Signup now to begin this journey and dive into the challenge details. Join a community of innovative thinkers, share ideas, and engage in this exciting challenge.

Challenges are more fun with teams. Find your teammate.
Have feedback or query? Share it with us.
Join the challenge now: AIcrowd | Amazon KDD Cup 2024: Multi-Task Online Shopping Challenge for LLMs | Challenges

All the best,
Team AIcrowd

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About the Amazon KDD Cup 2024: Multi-Task Online Shopping Ch category

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About the Amazon KDD Cup 2024: Multi-Task Online Shopping Ch category

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🧑‍💻 Office Hour for the Comprehensive RAG (CRAG) Challenge

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🧑‍💻 Office Hour for the Comprehensive RAG (CRAG) Challenge

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🧑‍💻 Office Hour for the Comprehensive RAG (CRAG) Challenge

Hello all,

We invite you to join the Office Hour for the Comprehensive RAG (CRAG) Challenge. This Office Hour is a chance to interact with the organisers, gain deep insights into the dataset and problem statement, and get your questions answered.

23rd April, 2024, 18:00 PST
Join the Office Hour on Zoom

For those unable to attend, a recording will be available. Feel free to post your questions here, and the organisers will answer them during the event.

Office Hour Highlights:

• Direct engagement with organisers
• Collaborative discussions with other attendees
• In-depth understanding of CRAG benchmarks
• What’s next in the challenge
• Live Q&A

Meet the speakers

• Xiao Yang: Applied Research Scientist at Meta Reality Labs, PhD in Statistics from Yale, focusing on retrieval augmented generation.
• Kai Sun: Research scientist at Meta, PhD from Cornell, organizer of Gomocup and chair for major NLP conferences.
• Xin Luna Dong: Principal Scientist at Meta, expert in building intelligent personal assistants and knowledge graphs, ACM and IEEE Fellow.

If you can’t attend, leave your questions in the comments, and the organisers will be answered during the session.

Mark your calendars, prepare your questions, and join the live Office Hour.

Looking forward to seeing you there!
Team AIcrowd

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💬 Welcome to KDD Cup 2024: CRAG Comprehensive RAG Benchmark Challenge

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About development set

You should be able to access the datasets in the Resources section of the individual Task pages.

Task 1 + Task 2 : AIcrowd | Meta KDD Cup 24 - CRAG - Retrieval Summarization | Challenges
Task 2: MockAPI
Task 3: AIcrowd | Meta KDD Cup 24 - CRAG - End-to-End Retrieval-Augmented Generation | Challenges

Best of Luck

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💬 Feedback & Suggestions

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👥 Looking for teammates?

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💬 Welcome to the Meta Comprehensive RAG Benchmark Challenge

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💬 Feedback & Suggestions

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👥 Looking for teammates?

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Tentative Challenge Winners

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Tentative Challenge Winners

Hello all,

Thank you for your participation in the Commonsense Persona-Grounded Dialogue Challenge. While we finalize the results through due diligence, we are pleased to announce the tentative winners for both tasks.

Please note that these are tentative results. We will notify you once the final winners are confirmed after the due diligence process is complete.

Best regards,
Team CPDC

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🏆 Generative Interior Design Challenge: Top 3 Teams

Dear Teams,

Thank you for participating in the Generative Interior Design Challenge! We are excited to announce the top three teams selected by an expert jury to advance to the final competition phase, which will take place on April 17 at the Machines Can See Summit in Dubai.

Here is the selection procedure we followed:

• Phase 1 (Jan 30 - Apr 1): Ranking based on the public test. All teams scoring above the baseline were selected for the next phase.
• Phase 2 (Apr 2 - Apr 3): Ranking based on the private test, with the top five teams advancing to the next phase for jury review.
• Phase 3 (Apr 4 - Apr 5): The expert jury ranked and selected the top three teams. Each jury member chose the best result among five generated images across six room categories and three empty scenes per category, doing so repeatedly. The names of the teams were concealed during the voting process. The three teams with the highest number of votes were chosen to proceed to the final phase.

Our jury consisted of experts in interior design, real estate development, and artificial intelligence.

As a result of Phases 1 and 2, the top five teams selected (in alphabetical order) are: Decem, EVATeam, Saidinesh_pola, StableDesign, and XenonStack.

Finally, the top three teams selected by the jury for Phase 3 (in alphabetical order) are:

• Decem (@decem, @littleduck007, @daniel_wang8)
• StableDesign (@Mykola_Lavreniuk, @bartosz_ludwiczuk)
• XenonStack (@xenonstack, @akashpandey_108, @xs296-piydhi)

These teams are now officially selected for the award. Congratulations!

We would like to note that the top three teams selected by the jury also rank among the top four on the public leaderboard of the competition.

We extend our thanks to all participating teams and look forward to the last competition phase on April 17 in Dubai. There, the final ranking will be determined jointly by the expert jury and the audience at the Machines Can See Summit.

Congratulations again, and we look forward to seeing everyone at Machines Can See on April 17th at the Museum of the Future!

Best wishes,
The Generative Interior Design Challenge Organizing Team

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🛍️ Welcome to KDD Cup: 2024 Multi-Task Online Shopping Challenge for LLMS

Hello,
Your query is answered here: Where is the ShopBench Amazon dataset?

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