Output Diversity in Language Models

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32 papers8 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Competition and Diversity in Generative AI
Dec 11, 2024Manish RaghavanOutput Diversity in Language Models Generative AI Evaluation
MIT Sloan School of Management & Department of Electrical Engineering and Computer Science.
Growing a Tail: Increasing Output Diversity in Large Language Models
Nov 5, 2024Michal Shur-Ofry, Bar Horowitz-Amsalem, Adir Rahamim +1LLM Evaluation LLM Prompting
Law Faculty, Hebrew University of Jerusalem; Jerusalem, Israel. · Faculty of Computer Science, Technion – Israel Institute of Technology; Haifa, Israel.

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Competition and Diversity in Generative AI

Manish Raghavan

MIT Sloan School of Management & Department of Electrical Engineering and Computer Science.

Output Diversity in Language Models Generative AI Evaluation

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Recent evidence, both in the lab and in the wild, suggests that the use of generative artificial intelligence reduces the diversity of content produced. The use of the same or similar AI models appears to lead to more homogeneous behavior. Our work begins with the observation that there is a force pushing in the opposite direction: competition. When producers compete with one another (e.g., for customers or attention), they are incentivized to create novel or unique content. We explore the impact competition has on both content diversity and overall social welfare. Through a formal game-theoretic model, we show that competitive markets select for diverse AI models, mitigating monoculture. We further show that a generative AI model that performs well in isolation (i.e., according to a benchmark) may fail to provide value in a competitive market. Our results highlight the importance of evaluating generative AI models across the breadth of their output distributions, particularly when they will be deployed in competitive environments. We validate our results empirically by using language models to play Scattergories, a word game in which players are rewarded for answers that are both correct and unique. Overall, our results suggest that homogenization due to generative AI is unlikely to persist in competitive markets, and instead, competition in downstream markets may drive diversification in AI model development.

Latest papers

Competition and Diversity in Generative AI

Growing a Tail: Increasing Output Diversity in Large Language Models