A Causality-aware Paradigm for Evaluating Creativity of Multimodal Large Language Models
Introduced LoTbench framework to evaluate creativity of multimodal LLMs using Oogiri game, finding the gap with humans is small.
Key Findings
Methodology
The paper introduces LoTbench, a causality-aware evaluation framework focused on assessing the creativity of multimodal large language models. This framework combines the Oogiri game with multi-round interaction and causal inference to quantify model creativity and visualize the underlying creative thought processes.
Key Results
- Result 1: LoTbench shows most multimodal LLMs have limited creativity, but the gap with humans is not large.
- Result 2: Strong correlation between LoTbench and multimodal cognition benchmark MMMU, but weak with traditional creativity metrics.
- Result 3: Current models still struggle with creative humor generation.
Significance
LoTbench provides a new perspective for evaluating the creativity of multimodal LLMs, emphasizing cognition as a critical foundation in early creativity stages. This research aids in understanding and enhancing AI performance in creative tasks, holding significant academic and practical value.
Technical Contribution
LoTbench introduces causal inference techniques to evaluate creativity, differing from traditional selection and ranking methods. Through multi-round interaction and causal analysis, LoTbench provides more interpretable evaluation results and reveals the model's innovative thinking process.
Novelty
LoTbench is the first framework to apply causal inference in evaluating the creativity of multimodal LLMs, overcoming limitations of traditional methods and aligning more closely with human cognitive theories.
Limitations
- Limitation 1: LoTbench may be limited by the scale and quality of the dataset used.
- Limitation 2: The complexity of causal inference may lead to high computational costs.
Future Work
Future research could explore optimizing the causal inference process in LoTbench to reduce computational costs and extend the framework to cover more creative tasks and multimodal scenarios.
AI Executive Summary
Recently, significant progress has been made in evaluating the logical reasoning abilities of large language models, but assessing their creativity remains challenging. Existing methods often overlook the subjective and diverse nature of creativity, especially in multimodal scenarios. This paper introduces a new evaluation framework, LoTbench, which uses the Oogiri game to assess the creativity of multimodal large language models.
The LoTbench framework integrates causal inference techniques to effectively quantify and visualize the creative thought processes of models. Experimental results show that while most models exhibit limited creativity, the gap with humans is not large. Additionally, LoTbench correlates strongly with the multimodal cognition benchmark MMMU, indicating its alignment with human cognitive theories.
This research provides new perspectives and methods for evaluating and enhancing AI creativity, with significant academic and practical implications. Future research could further optimize the causal inference process in LoTbench and expand its application scope.
Deep Analysis
Background
In recent years, significant progress has been made in evaluating the logical reasoning abilities of large language models, with representative methods including Chain-of-Thought (CoT). However, assessing creativity remains challenging, especially in multimodal scenarios. Existing methods often overlook the subjective and diverse nature of creativity.
Core Problem
Evaluating the creativity of multimodal large language models is complex, with challenges including the subjective nature of creativity, data scarcity, and the complexity of multimodal scenarios. This makes traditional evaluation methods ineffective in quantifying and interpreting model creativity.
Innovation
The core innovation of the LoTbench framework is the introduction of causal inference techniques to evaluate model creativity through multi-round interaction. This method not only quantifies model creativity but also provides visualizations of the creative thought process, offering higher interpretability compared to traditional methods.
Methodology
- �� Use the Oogiri game as an evaluation platform, challenging models' humor and associative thinking.
- �� Apply causal inference techniques to analyze models' creative thought processes during multi-round interactions.
- �� Quantify creativity based on the number of rounds needed to reach high-quality human-level creative responses.
Experiments
The experimental design includes standard and LoTbench evaluations using the Oogiri-GO dataset. Benchmarks include the multimodal cognition benchmark MMMU and traditional creativity metrics. The study compares different models' performances and conducts ablation studies.
Results
Experimental results indicate that LoTbench effectively quantifies model creativity, with a small gap compared to humans. LoTbench correlates strongly with MMMU, suggesting its alignment with human cognitive theories.
Applications
The LoTbench framework can be used to evaluate and enhance the performance of multimodal LLMs in creative tasks, applicable to scenarios requiring high creativity, such as creative writing and art generation.
Limitations & Outlook
LoTbench may be limited by the scale and quality of the dataset used. Additionally, the complexity of causal inference may lead to high computational costs. Future research could explore optimizing this process.
Plain Language Accessible to non-experts
Imagine you're playing a game called Oogiri, where you need to come up with funny responses based on given pictures or text. This game tests your creativity and humor. The LoTbench framework acts like a judge, evaluating your creativity by observing how you perform in the game. It's like a teacher in school who not only checks if your answer is correct but also looks at how you came up with it. LoTbench uses multiple rounds of interaction to see if you can come up with ideas that others might not think of.
ELI14 Explained like you're 14
Imagine you're playing a super fun game called Oogiri. You have to come up with funny responses based on pictures or text. LoTbench is like a super smart judge that watches how you come up with these funny answers. Just like in school, where teachers look at how you think, not just your answers. LoTbench uses multiple rounds to see if you can think of cool ideas that others might not. Isn't that awesome?
Glossary
Oogiri Game
A traditional Japanese creative game requiring participants to provide unexpected humorous responses to prompts.
Used to evaluate the creativity of multimodal large language models.
LoTbench
A causality-aware evaluation framework for quantifying and visualizing the creativity of multimodal large language models.
Used to assess models' creative thought processes.
Causal Inference
An analytical method used to determine causal relationships between variables.
Used in the LoTbench framework to evaluate model creativity.
Multimodal Large Language Models
Advanced language models capable of processing multiple types of data, such as text and images.
The main subject of study for creativity evaluation.
Creativity
The ability to generate novel and valuable ideas.
An important metric for evaluating multimodal large language models.
Open Questions Unanswered questions from this research
- 1 How to validate LoTbench's effectiveness on larger datasets?
- 2 How to reduce computational costs of causal inference for more efficient evaluation?
Applications
Immediate Applications
Creative Writing
LoTbench can help evaluate and enhance models' performance in creative writing tasks, suitable for tasks requiring high creativity.
Art Generation
By evaluating model creativity, LoTbench can be used in art generation applications to create more creative works.
Long-term Vision
Intelligent Creative Assistant
LoTbench can serve as the foundation for an intelligent creative assistant, providing inspiration and suggestions for various creative tasks.
Abstract
Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativity of multimodal LLMs, with a focus on suitable evaluation platforms and methodologies. First, we find the Oogiri game, a creativity-driven task requiring humor, associative thinking, and the ability to produce unexpected responses to text, images, or both. This game aligns well with the input-output structure of modern multimodal LLMs and benefits from a rich repository of high-quality, human-annotated creative responses, making it an ideal platform for studying LLM creativity. Next, beyond using the Oogiri game for standard evaluations like ranking and selection, we propose LoTbench, an interactive, causality-aware evaluation framework, to further address some intrinsic risks in standard evaluations, such as information leakage and limited interpretability. The proposed LoTbench not only quantifies LLM creativity more effectively but also visualizes the underlying creative thought processes. Our results show that while most LLMs exhibit constrained creativity, the performance gap between LLMs and humans is not insurmountable. Furthermore, we observe a strong correlation between results from the multimodal cognition benchmark MMMU and LoTbench, but only a weak connection with traditional creativity metrics. This suggests that LoTbench better aligns with human cognitive theories, highlighting cognition as a critical foundation in the early stages of creativity and enabling the bridging of diverse concepts. https://lotbench.github.io