Summary of Do Llms Agree on the Creativity Evaluation Of Alternative Uses?, by Abdullah Al Rabeyah et al.
Do LLMs Agree on the Creativity Evaluation of Alternative Uses?
by Abdullah Al Rabeyah, Fabrício Góes, Marco Volpe, Talles Medeiros
First submitted to arxiv on: 23 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Medium Difficulty summary: This paper investigates whether large language models (LLMs) can agree on assessing creativity in responses to the Alternative Uses Test (AUT). The study explores whether LLMs can impartially evaluate creative content generated by themselves and other models. The authors use an oracle benchmark set of AUT responses, categorized by creativity level, and experiment with four state-of-the-art LLMs evaluating these outputs using both scoring and ranking methods. The results reveal high inter-model agreement, with Spearman correlations averaging above 0.7 across models and reaching over 0.77 with respect to the oracle. This study suggests that LLMs exhibit impartiality and high alignment in creativity evaluation, offering promising implications for their use in automated creativity assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper looks at whether big language models can agree on how creative something is. They test these models by asking them to rate responses to a challenge called the Alternative Uses Test (AUT). The researchers want to know if the models are fair and consistent in their ratings, even when evaluating things they didn’t create themselves. To do this, they use a set of examples that have already been rated for creativity, and ask four different language models to rate them using two different methods. The results show that the models agree with each other pretty well, which is important because it means they can be trusted to give fair ratings. This could be useful in all sorts of areas where we need to automatically evaluate creative work. |
Keywords
» Artificial intelligence » Alignment
