Summary of Large Language Models Are Superpositions Of All Characters: Attaining Arbitrary Role-play Via Self-alignment, by Keming Lu et al.
Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
by Keming Lu, Bowen Yu, Chang Zhou, Jingren Zhou
First submitted to arxiv on: 23 Jan 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 This paper introduces Ditto, a self-alignment method for role-play that leverages the extensive knowledge of characters and potential dialogues within open-source large language models (LLMs). By fine-tuning an LLM using a self-generated dataset of 4,000 characters, Ditto enables the model to simulate role-play dialogues as a variant of reading comprehension. The study evaluates Ditto’s performance on a meticulously constructed and reproducible role-play benchmark, outperforming open-source baselines and achieving levels comparable to advanced proprietary chatbots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers talk like people by teaching them how to have conversations that feel real. It makes computer models better at playing pretend characters and having discussions. The researchers created a special way for these computer models to learn from themselves, using huge collections of text about different characters. They tested this new method on a special set of conversations and showed it can do as well as more advanced chatbots. |
Keywords
* Artificial intelligence * Alignment * Fine tuning
