Summary of Hansel: Output Length Controlling Framework For Large Language Models, by Seoha Song et al.
Hansel: Output Length Controlling Framework for Large Language Models
by Seoha Song, Junhyun Lee, Hyeonmok Ko
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 paper proposes Hansel, a framework for efficiently controlling the length of output sequences in large language models (LLMs) without affecting their generation ability. The approach utilizes special tokens and techniques to avoid abrupt termination, ensuring coherent and fluent generated text. Hansel can be applied to any pre-trained LLM during finetuning, regardless of original positional encoding methods. The framework demonstrates significant improvements in mean absolute error and extrapolation capabilities compared to prompt-based length control finetuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hansel is a new way to make large language models shorter or longer without affecting how well they generate text. It uses special tokens and clever tricks to keep the model from getting stuck. This helps make the generated text more natural-sounding and coherent. The best part? You can use Hansel with any pre-trained model, no matter what it was originally designed for. The results show that Hansel is really good at controlling sequence length and even works well when trying new lengths it didn’t see before. |
Keywords
» Artificial intelligence » Positional encoding » Prompt
