Summary of Cross-domain Content Generation with Domain-specific Small Language Models, by Ankit Maloo et al.
Cross-Domain Content Generation with Domain-Specific Small Language Models
by Ankit Maloo, Abhinav Garg
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper explores methods to enable a small language model to produce coherent and relevant outputs for two different domains: stories (Dataset A) and recipes (Dataset B). The authors find that training individual models on each dataset yields satisfactory results, with each model generating appropriate content within its domain. However, attempting to adapt a single model to both domains using LoRA or standard fine-tuning does not yield substantial results, often failing to produce meaningful outputs. To overcome this challenge, the authors employ a knowledge expansion strategy: training only with additional parameters. This approach enables the model to generate both stories and recipes upon request, effectively handling multiple domains without suffering from catastrophic forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make a small language model write good stories about recipes or stories using two different datasets. The researchers found that making separate models for each dataset worked well, but trying to make one model do both didn’t work very well. To fix this, they came up with a new way of training the model by adding more information without changing what it already knew. This helped the model write good stories about recipes or stories when asked. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Lora
