Summary of Language Models Show Stable Value Orientations Across Diverse Role-plays, by Bruce W. Lee et al.
Language Models Show Stable Value Orientations Across Diverse Role-Plays
by Bruce W. Lee, Yeongheon Lee, Hyunsoo Cho
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents research on large language models (LLMs) that exhibit consistent value orientations despite adopting diverse personas. The study introduces the “role-play-at-scale” methodology, which prompts LLMs to act in different random personas and asks the same question multiple times for each persona. This approach reveals consistent patterns in LLM responses across diverse role-play scenarios, indicating deeply encoded inherent tendencies. The findings contribute to the discourse on value alignment in foundation models and demonstrate the efficacy of role-play-at-scale as a diagnostic tool for uncovering encoded biases in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can have different personalities, but they still think in certain ways. Scientists studied these models by giving them different roles to play and asking them the same question multiple times. They found that the models always gave similar answers, even when playing different characters. This helps us understand how these models work and what biases they might have. |
Keywords
* Artificial intelligence * Alignment * Discourse
