Summary of Distilling Llms’ Decomposition Abilities Into Compact Language Models, by Denis Tarasov et al.
Distilling LLMs’ Decomposition Abilities into Compact Language Models
by Denis Tarasov, Kumar Shridhar
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The research aims to develop compact language models that can solve complex reasoning tasks while maintaining the scalability benefits of their larger counterparts. To achieve this, the study distills the decomposition skills of Large Language Models (LLMs) into smaller models using offline reinforcement learning. The approach leverages the LLM’s capabilities to provide feedback and generate a specialized dataset for training compact models. The primary contributions of this work are the development of an AI-generated dataset and the establishment of baselines, showcasing the potential of compact models in replicating complex problem-solving skills. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Compact language models can solve complex reasoning tasks with the help of offline reinforcement learning. This approach takes skills from Large Language Models (LLMs) and puts them into smaller models that are easier to use. The LLM helps provide feedback and creates a special dataset for training the compact models. This research shows how compact models can be used to solve tricky problems. |
Keywords
* Artificial intelligence * Reinforcement learning
