Summary of Configurable Safety Tuning Of Language Models with Synthetic Preference Data, by Victor Gallego
Configurable Safety Tuning of Language Models with Synthetic Preference Data
by Victor Gallego
First submitted to arxiv on: 30 Mar 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 proposed Configurable Safety Tuning (CST) method addresses the limitations of Direct Preference Optimization (DPO) in fine-tuning language models. CST introduces a system prompt to specify safety configurations, enabling deployers to dynamically adjust safety preferences. This allows for flexible deployment and retention of original functionality. Experimental evaluations demonstrate CST’s robustness in managing different safety configurations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to control the behavior of large language models. Currently, these models are fine-tuned using Direct Preference Optimization (DPO), which is like writing a set of instructions for how the model should behave. The problem with DPO is that it can’t be changed later on. To fix this, the researchers came up with a new method called Configurable Safety Tuning (CST). CST lets you adjust the safety settings of a language model by using a special prompt. This means you can turn certain features on or off depending on your needs. The authors tested their approach and found it worked well. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Optimization » Prompt
