Summary of Thinking Before Speaking: a Role-playing Model with Mindset, by Baohua Zhang et al.
Thinking Before Speaking: A Role-playing Model with Mindset
by Baohua Zhang, Yongyi Huang, Wenyao Cui, Huaping Zhang
First submitted to arxiv on: 14 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 proposed Thinking Before Speaking (TBS) model aims to improve Large Language Models’ (LLMs) ability to adopt a specific role by fine-tuning them using real-life scenarios, historical dialogue, and mindset. By supplementing each pair of dialogue with the character’s mindset, and adding data points that include elements beyond the role’s knowledge base, the TBS model encourages LLMs to think before speaking, rather than simply responding based on their training data. The approach is designed to help LLMs adopt the role’s thought process and logic, avoiding responses that fall outside the role’s knowledge base. To test this capability, a dataset and evaluation metrics have been prepared. Experimental results show that the TBS model can better emulate a role in terms of tone, knowledge, and mindset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Role-playing is an easy task for Large Language Models (LLMs), but they tend to perform poorly when confronted with knowledge they don’t possess or questions that require specific experience or logic. To make LLMs act more like real roles, researchers propose a new approach called Thinking Before Speaking (TBS). This method involves extending the data based on the character’s real-life scenarios and historical dialogue, then fine-tuning the LLMs. The goal is to help LLMs adopt the role’s thought process and logic, avoiding responses that fall outside their knowledge base. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge base
