Summary of Everything Everywhere All at Once: Llms Can In-context Learn Multiple Tasks in Superposition, by Zheyang Xiong et al.
Everything Everywhere All at Once: LLMs can In-Context Learn Multiple Tasks in Superposition
by Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios G Chrysos, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) have shown remarkable abilities in learning specific tasks within a given context. This paper investigates an unexpected phenomenon where LLMs can perform multiple, distinct tasks simultaneously during a single inference call, dubbed “task superposition.” The study provides empirical evidence of this capability across various LLM families and scales, demonstrating that it emerges even when training models to learn one task at a time. Additionally, the authors offer theoretical explanations for this phenomenon based on the expressive power of transformers. They also explore how LLMs internally combine task vectors during superposition, finding that larger models can execute more tasks in parallel while better calibrating their output distributions. This research sheds light on the latent capabilities of LLMs and further supports the idea of “LLMs as a superposition of simulators,” raising questions about the mechanisms enabling simultaneous task execution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are very smart! They can learn to do lots of things in one go, without needing to practice each thing separately. This is called “task superposition” and it’s like having multiple special powers at once! The scientists who did this study found out that even the biggest and most powerful language models can do many tasks together, which is really cool! They also learned how these models do their calculations when they’re working on lots of things at once. This helps us understand what makes language models so good at learning new things. |
Keywords
» Artificial intelligence » Inference
