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😃: [Text from: https://en.wikipedia.org/wiki/Rihanna]

Describe her Super Bowl performance and what made it special.
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🤖: Rihanna's Super Bowl LVII halftime show on February 12, 2023, marked a significant moment in her career, not only for its musical performance but also for its historical significance. It was a highly anticipated event, and Rihanna delivered a performance that was both visually stunning and musically powerful, solidifying her status as a cultural icon.

Musically, the performance was a blend of her signature hits and new material, showcasing her evolution as an artist. She opened with a powerful rendition of "Bitch Better Have My Money", a track known for its aggressive energy, setting the stage for a high-octane performance. The show featured a medley of her greatest hits, including "We Found Love", "Umbrella", "Diamonds", and "Work", each delivered with a mix of charisma and technical prowess.

Beyond the music, the performance was a visual spectacle, with elaborate costumes, choreography, and stage design. Rihanna's iconic style was on full display, with bold outfits and striking makeup. The show also featured a powerful message of empowerment and self-expression, reflecting her journey as an artist and a woman.

The Super Bowl performance was particularly significant for Rihanna as it marked her first live performance in over five years. She had taken a hiatus from touring and public appearances, focusing on her business ventures and personal life. Her return to the stage was met with widespread enthusiasm, showcasing her enduring popularity and artistic talent.

The performance also held historical significance as it made Rihanna the first woman to headline the Super Bowl halftime show since its inception in 1991. This achievement further cemented her status as a groundbreaking figure in popular culture, breaking barriers and paving the way for future female artists.

Overall, Rihanna's Super Bowl LVII halftime show was a triumph, a testament to her musical prowess, her enduring cultural impact, and her ability to captivate audiences with both her music and her persona. It was a performance that will be remembered for years to come, solidifying her place as one of the most influential and iconic figures in modern music.

😃: Đưa cho bạn dữ liệu sau, hãy dựa vào nó để trả lời câu hỏi ở cuối.
[Text from: https://vi.wikipedia.org/wiki/YouTube]

Câu hỏi: Với quảng cáo, Youtube sẽ lấy bao nhiêu % và chia lại bao nhiêu cho nhà sáng tác nội dung ?
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🤖: Theo thông tin được cung cấp, YouTube sẽ lấy 45% doanh thu từ quảng cáo của trang web và chia sẻ 55% còn lại cho nhà sáng tạo nội dung. Điều này có nghĩa là mỗi khi một video trên YouTube thu hút quảng cáo, nhà sáng tạo nội dung sẽ nhận được 55% lợi nhuận từ doanh thu quảng cáo đó, trong khi YouTube giữ lại 45% phần còn lại.

Đây là một mô hình kinh doanh phổ biến cho các nền tảng trực tuyến như YouTube, nơi họ thu hút người xem và tạo ra doanh thu từ quảng cáo, đồng thời chia sẻ một phần lợi nhuận với những người tạo nội dung để khuyến khích sự sáng tạo và duy trì sự đa dạng nội dung trên nền tảng.

😃: Give you the content of a research paper:
"""
[text of https://arxiv.org/html/2401.01325v3]
"""

Point out the highlights and identify the ideal people to apply it.
~
🤖: Highlights of the Research Paper "LLM Maybe LongLM: SelfExtend LLM Context Window Without Tuning"

This research paper presents a novel approach, SelfExtend, to enhance the ability of Large Language Models (LLMs) to handle long context sequences without the need for extensive fine-tuning. Here are the key highlights:

• Inherent Long Context Capabilities of LLMs: The paper challenges the prevailing belief that LLMs lack the ability to process long contexts. It argues that LLMs, particularly those with Rotary Position Embeddings (RoPE), possess inherent capabilities to understand and utilize long-range information. This is achieved by addressing the issue of positional Out-of-Distribution (O.O.D.), where LLMs struggle with unseen relative positions during inference.

• SelfExtend: A Plug-and-Play Solution: The paper introduces SelfExtend, a method that extends the context window of LLMs by introducing a two-tiered attention mechanism:

  • Grouped Attention: This mechanism utilizes a floor operation to map unseen large relative positions to those encountered during pretraining, effectively addressing the positional O.O.D. issue. This allows LLMs to handle longer sequences within their pretraining context window.
  • Standard Attention: To maintain the accuracy of local context understanding, SelfExtend retains the standard attention mechanism for tokens within a specified neighbor window. This ensures that the model can effectively capture the nuances of nearby information.

• Fine-Tuning-Free Context Window Extension: A significant advantage of SelfExtend is its ability to extend the context window of LLMs without requiring any fine-tuning or additional training. This makes it a highly practical and efficient solution for deploying LLMs in real-world scenarios where long context understanding is crucial.

• Empirical Validation: The paper provides comprehensive experimental evidence demonstrating the effectiveness of SelfExtend. It showcases significant performance improvements on various long-context tasks, including language modeling, synthetic long context benchmarks (LongBench, L-Eval), and real-world long context tasks. These improvements are achieved without sacrificing performance on standard short-context tasks, highlighting the versatility of SelfExtend.

Ideal People to Apply the SelfExtend Approach

The SelfExtend approach is particularly relevant for individuals and organizations involved in the following domains:

• Natural Language Processing (NLP): Researchers and practitioners working on NLP applications that require the processing of lengthy text sequences, such as:

  • Document Analysis: Summarization, information extraction, and sentiment analysis on long documents.
  • Long-Form Question Answering: Retrieval-based question answering systems that need to understand complex and lengthy contexts.
  • Retrieval Augmented Generation: Generation tasks where the model needs to access and integrate information from long context sequences.

• Language Model Development: Developers of LLMs who are seeking to improve the long-context capabilities of their models without the overhead of extensive fine-tuning.

• AI Applications: Companies and organizations leveraging AI for tasks that involve processing large amounts of text data, such as:

  • Customer Service: Chatbots and virtual assistants that need to understand and respond to complex customer inquiries.
  • Content Creation: Automated writing tools and content generation systems that require access to extensive context.

By enabling LLMs to effectively handle long contexts, SelfExtend has the potential to significantly enhance the performance and applicability of these AI-powered systems across various industries and domains.