Summary of Picle: Eliciting Diverse Behaviors From Large Language Models with Persona In-context Learning, by Hyeong Kyu Choi et al.
PICLe: Eliciting Diverse Behaviors from Large Language Models with Persona In-Context Learning
by Hyeong Kyu Choi, Yixuan Li
First submitted to arxiv on: 3 May 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 abstract proposes a novel approach to customize the behavior of Large Language Models (LLMs) by eliciting a desired personality trait from them. This is achieved through Persona In-Context Learning (PICLe), a framework grounded in Bayesian inference that optimizes model behavior to align with a target persona. The effectiveness of PICLe is demonstrated through comparisons against baseline methods across three contemporary LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are trained on huge amounts of text data, which can affect how they behave and what kind of personality traits they have. Researchers want to know if it’s possible to make these models act in a specific way by “eliciting” a certain personality from them. They developed a new method called Persona In-Context Learning (PICLe) that uses special math calculations to help the model learn how to behave like someone with a particular persona. The researchers tested PICLe on three different models and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Bayesian inference
