GraphReader: A Graph-based AI Agent System Designed to Handle Long Texts by Structuring them into a Graph and Employing an Agent to Explore this Graph Autonomously
How do model-level methods address LLMs' long-context challenges?
Model-level methods tackle LLMs' long-context challenges by modifying the models themselves. Approaches such as positional interpolation and transformer variants with altered attention mechanisms aim to enhance the model's ability to handle long sequences. However, these methods may increase training costs, neglect detailed information, or lose earlier context.
What limitations do LLMs face with long contexts?
Large language models (LLMs) struggle with long contexts due to limitations in context window size and memory usage, hindering their ability to process and comprehend extensive text inputs effectively. Existing methods like model-level approaches and retrieval-based methods have drawbacks such as increased training costs and limitations in decision-making mechanisms.
What drawbacks do transformer variants present in LLMs?
Transformer variants in LLMs can present drawbacks such as increased training costs, neglect of detailed information, and loss of earlier context. These issues may arise due to modifications in the attention mechanisms or other components of the Transformer architecture, which aim to improve long-context processing but can compromise efficiency or accuracy in certain aspects.