Summary of Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models, by Minki Kang et al.
Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models
by Minki Kang, Sung Ju Hwang, Gibbeum Lee, Jaewoong Cho
First submitted to arxiv on: 1 Nov 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 This paper introduces LaPael, a method for improving knowledge injection in Large Language Models (LLMs) by applying input-dependent noise to early LLM layers. This approach enables diverse and semantically consistent augmentations within the model, reducing computational costs and sample diversity limitations. The authors demonstrate that LaPael outperforms standard fine-tuning and existing noise-based approaches on question-answering benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make language models smarter by adding new ideas to them quickly and efficiently. Right now, people usually fine-tune these models with rewritten data, but this can be slow and doesn’t give enough variety. To fix this, the researchers created LaPael, a way to add noise to the model’s early layers based on what it’s trying to learn. This makes the training process faster and gives the model more diverse ideas. The results show that LaPael works better than other methods for improving language models. |
Keywords
» Artificial intelligence » Fine tuning » Question answering
