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Signed-off-by: Haiming94 <931284009@qq.com>
Summary of ChangesHello @Haiming94, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request refactors the documentation structure for the LongContext-ICL-Annotation challenge. It simplifies both the English and Chinese README files by removing detailed introductory and challenge objective sections, focusing instead on providing concise news updates and quick start guides. The aim is to make the initial setup information more direct and accessible within the repository. Highlights
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Code Review
The pull request updates the documentation by removing detailed introductory, objective, and challenge detail sections from both the English and Chinese READMEs, focusing instead on a "Quick Start" guide. While streamlining documentation can be beneficial, the removal of these comprehensive sections might reduce the clarity and context for new users trying to understand the competition's scope and rules. It would be advisable to ensure that this critical information is still easily accessible, perhaps by linking to a dedicated competition page or a more detailed project overview.
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| ## Introduction | ||
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| The LongContext-ICL-Annotation Challenge focuses on automatic data annotation under long-context settings using Large Language Models (LLMs). The competition is built upon the Qwen3-4B model and adopts the In-context Learning (ICL) paradigm to investigate scalable and high-quality automated annotation methods. | ||
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| Participating teams are required to use the officially provided datasets and design effective ICL-based annotation solutions tailored for ultra-long context scenarios. All submissions will be evaluated on a unified benchmark dataset. The Organizing Committee will conduct standardized evaluations and determine the final rankings based on the official evaluation results. | ||
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| ## Objectives | ||
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| This challenge takes Large Language Models (LLMs) as the core technical foundation and targets automated data annotation under ultra-long context constraints, aiming to explore novel paradigms that balance annotation efficiency and annotation accuracy. The competition focuses on the following key scientific and engineering challenges: | ||
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| - 1. Instruction and Prompt Design: | ||
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| How can effective model instructions and prompt strategies be designed in ultra-long context scenarios to guide LLMs toward stable and high-quality data annotation? | ||
| - 2. Ultra-Long Context Construction: | ||
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| When the number of available annotation examples significantly exceeds the model’s context capacity, how can information-dense and structurally coherent ultra-long context inputs be constructed for target data annotation? | ||
| - 3. Multi-Turn and Continuous Annotation: | ||
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| In automated multi-round dialogue or continuous interaction settings, how can ultra-long contexts be efficiently leveraged to achieve both consistency and scalability in data annotation? | ||
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| ## Challenge Details | ||
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| - Participating teams are expected to independently design a complete LLM-based automatic data annotation pipeline and validate their approach under a unified dataset and evaluation protocol. Evaluation scores and rankings will be published on a standardized leaderboard. | ||
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| - In addition to prediction results, teams must submit a technical report and fully reproducible source code in accordance with the competition requirements. The Organizing Committee will reproduce submitted solutions and review the technical design. The final score will be calculated as a weighted combination of prediction performance and technical solution evaluation, with detailed rules specified by the competition. | ||
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| - Teams are required to submit their technical reports and complete source code to the official OpenSeek GitHub repository designated by the competition. | ||
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| - For additional details, please refer to [FlagOS platform](https://flagos.io/RaceDetail?id=296fmsd8&lang=en). All competition-related information is subject to the announcements published on the official platform. | ||
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The removal of the "Introduction", "Objectives", and "Challenge Details" sections significantly reduces the contextual information available to users. These sections are crucial for new participants to understand the competition's purpose, goals, and detailed rules. While a "Quick Start" guide is valuable, a comprehensive overview is equally important for proper engagement and understanding of the project. Consider retaining a condensed version of these sections or providing a clear link to where this information can be found.
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