Summary of our paper ARAGOG: Advanced RAG Output Grading
By decoupling the retrieval and synthesis processes and introducing innovative methods such as Sentence-window Retriever, Auto-merging Retrieval, and Document Summary, we significantly improve the LLM's ability to generate precise, contextually rich responses.
Query expansion tehcniques, such as Hypothetical Answer and Multi-Query, offer promising avenues for enhancing the performance of language models by facilitating more relevant and accurate information retrieval. By leveraging these sophisticated methods, we can push the boundaries of what's possible with LLMs, leading to more precise and useful responses to complex queries. Stay tuned for more advanced RAG techniques!
The true value of RAG lies in its ability to grant LLMs access to previously unseen internal datasets. This access is pivotal for organizations that need to utilize their proprietary data for enhanced decision-making. By integrating RAG, LLMs can generate responses that are not only accurate but also tailored to the specific context and knowledge base of a business.
SOLAR 10.7B's introduction showcases a transformative step in LLMs, blending Llama 2's architecture with Mistral 7B's weights for unparalleled performance. Notably, its success in single-turn conversations, as reflected by its impressive Model H6 score, marks a new industry benchmark. This breakthrough underscores South Korea's rising prominence in AI, promising innovative applications of LLMs across diverse fields.
The synergy between sensitive data and LLMs marks a significant step forward in sectors like healthcare and finance. The insights derived from data can revolutionize services and outcomes. However, this journey must be underpinned by a strong commitment to ethical data use, robust protection strategies, and respect for privacy. In this article we explore three approaches to combine LLMs with sensitive data, while protecting data integrety.
The introduction of Google's Gemini model to the competitive landscape of Large Language Models, with its advanced multimodal capabilities, is a noteworthy event. Its impressive performance could be a game-changer if further evaluations uphold Google's claims. However, the model's true standing, particularly in comparison to GPT-4, will hinge on unbiased, independent validation in the times ahead.
Microsoft's Phi-2 model represents a significant shift in the landscape of Large Language Models, challenging the notion that bigger models always equate to better performance. With its 2.7 billion parameters, Phi-2 rivals the performance of much larger models like Llama and Mistral, underscoring the power of its meticulously curated 'textbook-quality' training dataset.
This model, with its unique Mixture of Experts (MoE) architecture, marks a significant advancement, offering a blend of efficiency and capability that challenges even the best open-source LLMs. Key Highlights of Mixtral 8x7b: Unparalleled performance in the open-source domain. The MoE architecture enhances efficiency and processing speed. High VRAM requirements orient it towards research and enterprise applications. While Mixtral 8x7b may not rival the giants like GPT-4 in all aspects, it represents a significant step for open-source AI, hinting at a future where powerful AI tools are more accessible. Discover the depths of Mixtral 8x7b in our latest deep dive
Choosing between convenient proprietary or customizable open source large language models involves balancing rapid prototyping against long-term costs and data security. The optimal approach depends on use case breadth and security needs.
LiQA leverages AI to transform enterprise document search. Proprietary files are ingested, converted to vectors, and indexed for personalized QA. Questions retrieve relevant excerpts to contextualize answers. Ongoing improvements will enhance accuracy, efficiency, and knowledge sources. LiQA unlocks the potential of your organization's documents.
Our team explored using LLMs like GPT3.5 for controlled content generation from seed data. We designed prompts and evaluation methods to quantify quality. LLMs possess great potential but need guidance. Exciting times ahead!
Stockholm, May 28, 2021 — Predli announced today a collaboration with the AI for Good Foundation to accelerate work on the UN Sustainable Development Goals (UNSDGs) and address the most pressing challenges faced by our communities.
While we see manufacturers fiddling with AI and machine learning, Industry 4.0 is still a moonshot for many, including top Fortune 500 companies. The reasoning is simple, too many companies are stuck in the “pilot purgatory” phase. This is the state where companies have an idea that has moved to the proof of concept (PoC) phase, but instead of reaching customers, it ends up at the infamous PoC graveyard.
