Graph-enhanced Large Language Models | Enhancing LLM Performance | Fangru Lin, DPhil NLP @UniOxford скачать в хорошем качестве

Graph-enhanced Large Language Models | Enhancing LLM Performance | Fangru Lin, DPhil NLP @UniOxford

📜 Abstract: The "Plan Like a Graph (PLaG)" method introduces a novel approach to improving large language models (LLMs) by breaking down tasks into sub-tasks, which are then arranged into an execution graph. This approach allows for both parallel and sequential execution of tasks, significantly enhancing the performance of LLMs without the need for fine-tuning. Fangru Lin's research showcases how this method can be applied to both closed and open-source LLMs, including GPT-4 and LLaMA-2, achieving state-of-the-art results. The study highlights the method's ability to improve performance even in the face of increasing task complexity, making it a promising direction for future AI research. 🧑‍🎓 Speaker Bio: Fangru Lin is a DPhil NLP student at the University of Oxford and a Clarendon scholar, with extensive experience in natural language processing. Her research has garnered significant attention and sparked engaging discussions within the AI community. 🎯 Why Attend? This talk provides a unique opportunity to gain insights into cutting-edge techniques in AI and LLMs, particularly for those interested in advanced prompt engineering and complex reasoning tasks. Whether you're familiar with the PLaG method or new to it, this session promises to broaden your understanding and offer practical applications for enhancing LLM performance. 📢 Call to Action: We invite you to participate in this insightful session and engage with the latest advancements in AI research. Additionally, please support us by interacting with our related LinkedIn posts, helping to amplify the reach and impact of these valuable discussions within the community. LinkedIn : https://www.linkedin.com/posts/comput... YT :    / @computervisiontalks4659