Summary Language Models Represent Space and Time arxiv.org
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One Line
Large language models learn linear representations of space and time, with improved performance as the models increase in size.
Slides
Slide Presentation (8 slides)
Key Points
- Large language models (LLMs) have the ability to learn structured knowledge about fundamental dimensions such as space and time.
- LLMs learn linear representations of space and time across multiple scales, which are robust and unified across different entity types.
- Probing experiments show that spatial and temporal representations are built throughout the early layers of LLMs before plateauing.
- Larger LLMs consistently outperform smaller ones in predicting real-world location or time.
- Individual neurons within LLMs are highly sensitive to the true location of entities, providing evidence that LLMs make use of spatial and temporal features.
Summaries
17 word summary
Large language models (LLMs) learn linear representations of space and time, with better performance in larger models.
72 word summary
Researchers found that large language models (LLMs) acquire knowledge about space and time, learning linear representations across multiple scales. Spatial and temporal representations are built in early layers before plateauing. Larger models outperform smaller ones, with robust and unified representations. Generalization performance was better than random, although not in absolute position. Individual neurons within LLMs are highly sensitive to the true location of entities, suggesting further investigation into spatial and temporal representations.
147 word summary
Researchers conducted a study on large language models (LLMs) to determine if they acquire knowledge about space and time. The study found evidence that LLMs learn linear representations of space and time across multiple scales. Probing experiments revealed that spatial and temporal representations are built throughout the early layers of the models before plateauing. Larger models outperformed smaller ones, and the representations were linear and robust to changes in prompting. The representations were also unified across different types of entities. The researchers conducted robustness checks and found that generalization performance was better than random, although not in absolute position. Individual neurons within the LLMs were highly sensitive to the true location of entities, supporting the idea that the models make use of spatial and temporal features. The study contributes to understanding how LLMs model the world and suggests further investigation into spatial and temporal representations in LLMs.
369 word summary
Researchers conducted a study to analyze the learned representations of spatial and temporal datasets in large language models (LLMs) to determine if LLMs acquire structured knowledge about fundamental dimensions such as space and time. They found evidence that LLMs learn linear representations of space and time across multiple scales and identified individual "space neurons" and "time neurons" that reliably encode spatial and temporal coordinates, providing further evidence that LLMs learn literal world models.
The researchers constructed six datasets containing names of places or events with corresponding space or time coordinates. Probing experiments revealed that models build spatial and temporal representations throughout the early layers before plateauing at around the halfway point. Larger models consistently outperformed smaller ones, and the representations were found to be linear and robust to changes in prompting. The representations were also unified across different types of entities.
To verify their findings, the researchers conducted robustness checks and found that while generalization performance suffered when specific blocks of data were held out, it was still better than random. The probes correctly generalized by placing points in the correct relative position but not in their absolute position. The researchers also trained probes with fewer parameters, which supported the idea that LLMs explicitly represent space and time but require more parameters to convert from the model's coordinate system to literal spatial coordinates or timestamps.
Additionally, the researchers discovered individual neurons within the LLMs that were highly sensitive to the true location of entities in space or time. These neurons were themselves fairly predictive feature probes, providing strong evidence that the models have learned and make use of spatial and temporal features.
The study contributes to the understanding of how LLMs model the world and supports the view that they learn more than superficial statistics. The researchers suggest future work to further investigate the structure and use of spatial and temporal representations in LLMs, as well as explore the potential for sparse autoencoders to extract representations in the model's coordinate system. They also highlight the need for methods to identify when a model recognizes a particular entity beyond specific prompts and recommend studying the training process to understand how spatial and temporal features are learned, recalled, and used internally.
508 word summary
Large language models (LLMs) have sparked debate over their capabilities and whether they merely learn superficial statistics or a coherent model of the data generating process. In a study, researchers analyzed the learned representations of spatial and temporal datasets in the Llama-2 family of models to determine if LLMs acquire structured knowledge about fundamental dimensions such as space and time. The researchers found evidence that LLMs learn linear representations of space and time across multiple scales. These representations are robust to prompting variations and unified across different entity types. They also identified individual "space neurons" and "time neurons" that reliably encode spatial and temporal coordinates, providing further evidence that LLMs learn literal world models.
The researchers constructed six datasets containing the names of places or events with corresponding space or time coordinates. These datasets spanned multiple spatiotemporal scales, including locations within the whole world, the United States, and New York City, as well as the death year of historical figures, the release date of art and entertainment, and the publication date of news headlines. They used linear regression probes on the internal activations of the names of these places and events at each layer of the Llama-2 models to predict their real-world location or time.
The probing experiments revealed that models build spatial and temporal representations throughout the early layers before plateauing at around the halfway point. Larger models consistently outperformed smaller ones. The representations were found to be linear, as nonlinear probes did not perform better. The representations were also fairly robust to changes in prompting and were unified across different types of entities.
To verify their findings, the researchers conducted robustness checks. They found that while generalization performance suffered when specific blocks of data were held out, it was still better than random. The probes correctly generalized by placing points in the correct relative position but not in their absolute position. The researchers also trained probes with fewer parameters by projecting the activation datasets onto their k largest principal components. The performance of these probes supported the idea that LLMs explicitly represent space and time but require more parameters to convert from the model's coordinate system to literal spatial coordinates or timestamps.
Additionally, the researchers discovered individual neurons within the LLMs that were highly sensitive to the true location of entities in space or time. These neurons were themselves fairly predictive feature probes, providing strong evidence that the models have learned and make use of spatial and temporal features.
The study contributes to the understanding of how LLMs model the world and supports the view that they learn more than superficial statistics. The researchers suggest future work to further investigate the structure and use of spatial and temporal representations in LLMs, as well as explore the potential for sparse autoencoders to extract representations in the model's coordinate system. They also highlight the need for methods to identify when a model recognizes a particular entity beyond specific prompts and recommend studying the training process to understand how spatial and temporal features are learned, recalled, and used internally.