Summary of All Language Models Large and Small, by Zhixun Chen et al.
All Language Models Large and Small
by Zhixun Chen, Yali Du, David Mguni
First submitted to arxiv on: 19 Feb 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 This paper introduces the Language Optimising Network Distribution (LONDI) framework, a novel plug-and-play language model (LM) that selectively employs large LMs for complex decision-making and reasoning tasks while using low-resource LMs elsewhere. The framework consists of two off-policy networks, an LM, a large LM (LLM), and a reinforcement learning module that uses switching controls to quickly learn which system states require the LLM’s activation. A variant of LONDI maintains budget constraints on LLM calls, theoretically proving that it learns to activate the LLM only when necessary to solve tasks. Experimental results demonstrate LONDI’s ability to reduce GPU usage by up to 30% while solving tasks solvable by resource-intensive LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to use language models more efficiently. It’s like having different tools for different jobs, and using the right tool for each task. The new method is called LONDI, and it helps reduce the amount of computer power needed to do certain tasks. This is important because some tasks require a lot of computer power, which can be expensive and slow down other tasks. The paper shows that LONDI can solve problems that need a lot of computer power while using much less power than before. |
Keywords
* Artificial intelligence * Language model * Reinforcement learning
