Summary of Do Llms Have Consistent Values?, by Naama Rozen et al.
Do LLMs have Consistent Values?
by Naama Rozen, Liat Bezalel, Gal Elidan, Amir Globerson, Ella Daniel
First submitted to arxiv on: 16 Jul 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 Large Language Models (LLMs) are designed to generate human-like dialogue, but researchers have yet to study the values exhibited by these models. This paper bridges this gap by analyzing whether LLMs exhibit a similar value structure as humans. The study finds that the results depend on how the model is prompted, and that under a specific prompting strategy (“Value Anchoring”), there is significant agreement with human data. These findings contribute to a better understanding of values in LLMs and introduce new methods for assessing consistency in their responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can talk like humans, but scientists haven’t studied what they value. This paper looks at whether these models have the same values as people do. They found that how you ask the model to answer affects the results, and when you ask it to “anchor” its values, the answers match what people would say. This helps us understand more about LLMs’ values and how they work. |
Keywords
» Artificial intelligence » Prompting
