• Diagram titled "12 consecutive months of record-breaking temperatures" showing "Monthly global average temperature by year, 1940-2024". • The diagram plots monthly average temperatures from June to May, with each month represented by a horizontal scatter of dots. • Dots are colored from yellow to dark red, indicating decades from the 1940s to the 2020s, as explained by a legend at the top. • A dashed line highlights the "most recent 12 months" from September 2023 to May 2024, with specific temperature values for May 2024 (15.91C). • The x-axis shows temperature in Celsius, ranging from 12C to 17C. • Source information at the bottom left reads: "Source: ERA5, C3S/ECMWF © FT".

**Summary of "Mapping LLM Embeddings in Three Dimensions"** - **Purpose**: The research explores visualizing and understanding Large Language Model (LLM) embeddings by mapping them in 3D space, aiming to provide insights into LLM's internal representations and improve interpretability. - **Methodology**: The authors use the t-SNE (t-Distributed Stochastic Neighbor Embedding) algorithm to reduce the high-dimensional embeddings to 3D, enabling visual exploration. - **Key Findings**: - **Clustering**: LLM embeddings cluster based on semantic similarity, with related words (e.g., 'king' and 'queen') close to each other in the 3D space. - **Manifold Structure**: Embeddings form a continuous, manifold-like structure, with semantically similar words lying on connected paths. - **Siamese Relationships**: Words with strong semantic relationships (e.g., 'Paris' and 'France') appear close together, forming 'siamese' pairs. - **Visualizations**: The authors provide interactive 3D visualizations, allowing users to rotate, zoom, and explore the embedding space. These visualizations can be found at <https://projects.stephenkay.co/llm-embeddings/>. - **Interpretability**: By examining the 3D maps, researchers can gain insights into LLM's understanding of language, such as its grasp of semantic relationships and analogies (e.g., 'man : king :: woman : queen'). - **Limitations**: The 3D visualization is a simplification of the high-dimensional embedding space, and some information may be lost or distorted during the dimensionality reduction process. - **Future Work**: The authors suggest exploring other dimensionality reduction techniques, dynamic embeddings, and incorporating task-specific information to enhance the visualizations' utility. - **Why Saved**: This research was saved due to its potential in improving our understanding of LLMs, enabling better interpretability, and facilitating more informed model development and fine-tuning. The interactive visualizations also provide a compelling way to explore and communicate LLM embeddings.