Summary of Evaluating the Prompt Steerability Of Large Language Models, by Erik Miehling et al.
Evaluating the Prompt Steerability of Large Language Models
by Erik Miehling, Michael Desmond, Karthikeyan Natesan Ramamurthy, Elizabeth M. Daly, Pierre Dognin, Jesus Rios, Djallel Bouneffouf, Miao Liu
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 benchmark evaluates the steerability of model personas as a function of prompting, aiming to design pluralistic AI that can represent various value systems and cultures. The authors define prompt steerability by analyzing the degree to which a model’s joint behavioral distribution can be shifted from its baseline. They introduce steerability indices and demonstrate how these indices change with steering effort across persona dimensions and directions. Results reveal limited steerability in many current models, mainly due to skewed baselines and asymmetric steerability across persona dimensions. The authors release an implementation of the benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making AI that can work well for different people with different values and cultures. To do this, we need to figure out how good AI models are at changing their behavior based on what they’re told. The researchers came up with a way to measure this ability called prompt steerability. They also found out that many current AI models aren’t very good at adapting to different perspectives. |
Keywords
» Artificial intelligence » Prompt » Prompting
