Summary of Ruler: Improving Llm Controllability by Rule-based Data Recycling, By Ming Li et al.
RuleR: Improving LLM Controllability by Rule-based Data Recycling
by Ming Li, Han Chen, Chenguang Wang, Dang Nguyen, Dianqi Li, Tianyi Zhou
First submitted to arxiv on: 22 Jun 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 A novel approach to enhancing the controllability of large language models (LLMs) is proposed, addressing the current reliance on human experts or proprietary LLMs for curating supervised fine-tuning datasets. The Rule-based Data Recycling (RuleR) method incorporates multiple constraints into original data samples according to predefined rules, creating new training tasks that consolidate LLM controllability. This approach “recycles” existing data by applying rule-based edits to responses and appending the rule-instructions in their original instructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can’t always control what they say, which is important for making them better and more useful. To make this happen without needing lots of extra money or complicated LLMs, a new method called RuleR was developed. This method takes existing data and makes changes to it according to specific rules, then uses the changed data to teach the LLM how to be more controlled in its responses. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
