Summary of Pretrained Generative Language Models As General Learning Frameworks For Sequence-based Tasks, by Ben Fauber
Pretrained Generative Language Models as General Learning Frameworks for Sequence-Based Tasks
by Ben Fauber
First submitted to arxiv on: 8 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 proposed framework utilizes small, pretrained generative language models with millions of parameters as a general learning framework for sequence-based tasks. This approach overcomes challenges associated with training neural networks and language models from scratch, offering a more efficient solution. The framework focuses on creating highly specialized models that can accurately execute challenging tasks. We demonstrate the effectiveness of this approach by instruction fine-tuning small language models with 10,000-to-1,000,000 examples to achieve near state-of-the-art results on cheminformatics tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to use language models for different tasks. Instead of training a model from scratch, scientists can start with a pre-trained model and make small changes to help it learn specific skills. This approach is useful because it saves time and computing resources. The researchers tested this idea by fine-tuning three different language models and achieved good results on chemistry-related tasks. |
Keywords
* Artificial intelligence * Fine tuning
