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Google’s new technique gives LLMs infinite context: A new paper by researchers at Google claims to give large language models (LLMs) the ability to work with text of infinite length. The paper introduces Infini-attention, a technique that configures language models in a way that extends their “context window” while keeping memory and compute requirements constant. https://lnkd.in/d7NARMnK

Jose Lara

Eye-opening research. Successfully enhancing spatial reasoning in LLMs by opening their "Mind's Eye". 

To this day, the ability of Large Language Models (LLMs) to understand and reason about space remains relatively unexplored.

Inspired by the human cognitive process of visualizing unseen objects and actions, known as the "Mind's Eye," researchers have proposed a new prompting technique called Visualization-of-Thought (VoT).

VoT guides LLMs through multi-hop spatial reasoning tasks by visualizing their reasoning traces at each step. 

This approach has shown significant improvements in tasks such as natural language navigation, visual navigation, and visual tiling in 2D grid worlds.

Surprisingly, VoT seems to outperform even multimodal LLMs (MLLMs) in these tasks, despite MLLMs being designed to handle both language and visual information.

This finding suggests that the ability to generate "mental images" to facilitate reasoning, much like the human mind's eye, could be a viable approach for MLLMs as well.

To learn more about VoT and other paper highlights, check out this week's newsletter:

https://lnkd.in/dEP6ycwN

Visualization-of-Thought: Eliciting LLM Spatial Reasoning

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