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Towards AI@Towards AI
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Towards AI is at the forefront of developing AI systems capable of self-correction, a crucial step towards more reliable and robust artificial intelligence. The publication highlights techniques such as Corrective RAG, which aims to improve generation by integrating a self-correction mechanism, and Adaptive RAG, a system designed to dynamically route user queries based on their complexity and feedback loops. These advancements are critical for addressing limitations in current AI models, ensuring that systems can recover from errors and provide more accurate outputs, even when faced with challenging or ambiguous inputs.
One key area of focus is the improvement of Retrieval-Augmented Generation (RAG) systems. Traditional RAG, while powerful, can be hindered by irrelevant or inaccurate retrieved documents, leading to poor responses. Corrective RAG addresses this by grading retrieved documents for usefulness and rewriting queries when necessary, ensuring a more accurate path to the desired answer. This concept is likened to Google Maps with live traffic updates, constantly checking and rerouting to avoid issues, a significant upgrade from a GPS that sticks to its initial route regardless of real-world conditions.
Furthermore, Towards AI is exploring methods to enhance AI decision-making through reinforcement learning. Techniques like Real-Time PPO are being developed to adapt dynamic pricing models effectively, ensuring stability in volatile environments. The publication also touches upon the application of fine-tuning small language models to think with reinforcement learning, acknowledging the challenges of imbuing smaller models with the common sense reasoning found in larger counterparts. This involves employing additional techniques beyond raw compute power to foster logical and analytical capabilities. The initiative also showcases practical applications like building financial report retrieval systems using LlamaIndex and Gemini 2.0, and the development of AI legal document assistants, demonstrating the breadth of their commitment to advancing AI capabilities.
References :
One key area of focus is the improvement of Retrieval-Augmented Generation (RAG) systems. Traditional RAG, while powerful, can be hindered by irrelevant or inaccurate retrieved documents, leading to poor responses. Corrective RAG addresses this by grading retrieved documents for usefulness and rewriting queries when necessary, ensuring a more accurate path to the desired answer. This concept is likened to Google Maps with live traffic updates, constantly checking and rerouting to avoid issues, a significant upgrade from a GPS that sticks to its initial route regardless of real-world conditions.
Furthermore, Towards AI is exploring methods to enhance AI decision-making through reinforcement learning. Techniques like Real-Time PPO are being developed to adapt dynamic pricing models effectively, ensuring stability in volatile environments. The publication also touches upon the application of fine-tuning small language models to think with reinforcement learning, acknowledging the challenges of imbuing smaller models with the common sense reasoning found in larger counterparts. This involves employing additional techniques beyond raw compute power to foster logical and analytical capabilities. The initiative also showcases practical applications like building financial report retrieval systems using LlamaIndex and Gemini 2.0, and the development of AI legal document assistants, demonstrating the breadth of their commitment to advancing AI capabilities.
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References :
- pub.towardsai.net: LAI #83: Corrective RAG, Real-Time PPO, Adaptive Retrieval, and LLM Scaling Paths
- Towards AI: LAI #83: Corrective RAG, Real-Time PPO, Adaptive Retrieval, and LLM Scaling Paths
- medium.com: Corrective RAG: How to Build Self-Correcting Retrieval-Augmented Generation