- The paper introduces ReMI, a benchmark dataset that evaluates LLMs’ multi-image reasoning across diverse tasks including algebra, physics, and logic.
- It reveals substantial performance gaps between state-of-the-art models and human reasoning, particularly in interleaved and sequential image tasks.
- Detailed failure analysis identifies common pitfalls such as calculation errors and misinterpretation of visual cues, guiding future improvements in multi-modal reasoning.
ReMI: A Dataset for Reasoning with Multiple Images
The paper introduces ReMI, a novel benchmark dataset aimed at evaluating the ability of LLMs to reason with multiple images. Given the continuous advancements in LLMs and their growing capabilities in multi-modal reasoning, this benchmark addresses the emergent need for specialized evaluation frameworks that extend beyond single-image reasoning. The research provides an in-depth analysis of the performance of state-of-the-art LLMs on this dataset and reveals substantial performance gaps compared to human-level proficiency, emphasizing the challenges and potential areas for future improvements in multi-image reasoning.
Introduction
The introduction highlights the rapid advancements in LLMs, especially their increasing ability to handle complex reasoning tasks across various domains. Prior benchmarks have primarily focused on single-image reasoning, neglecting the emerging capabilities of multi-image reasoning. Consequently, this paper introduces ReMI as a comprehensive benchmark designed specifically to evaluate and enhance the multi-image reasoning skills of LLMs. The necessity of such a benchmark is underlined by the diverse applications and the multi-faceted nature of reasoning tasks that extend beyond the capabilities of current single-image benchmarks.
Dataset Description
ReMI encompasses a wide range of reasoning domains including algebra, calculus, geometry, physics, and logic, among others. It is meticulously designed to test various key properties unique to multi-image reasoning:
- Sequential vs Set Consumption: Tasks where images need to be processed in a specific sequence versus tasks where images are treated as a set.
- Same vs Different Concepts: Tasks that involve reasoning with images representing the same concept versus tasks with images representing different concepts.
- Interleaving: Tasks where images are interleaved with question text versus tasks where all images are provided upfront.
- Number of Images: Tasks that require reasoning over varying numbers of images.
The dataset comprises 13 distinct tasks, each carefully crafted to cover these properties, thus providing a robust and diverse testbed for evaluating the multi-image reasoning capabilities of LLMs. The images in ReMI are varied and include charts, tables, equations, emojis, graphs, shapes, maps, and more, reflecting the heterogeneity found in real-world multi-image reasoning scenarios.
Experimental Evaluation
The paper benchmarks several state-of-the-art LLMs including models from the Gemini series, Claude 3, and GPT-4 Turbo. The results demonstrate a significant performance gap between model performance and human performance, highlighting the current limitations of LLMs in achieving human-level proficiency in multi-image reasoning.
Key Findings:
- Performance Comparison: All evaluated models significantly outperform naive baselines, but still lag behind human performance. This gap is particularly pronounced in tasks like Clocks and Isomorphism, indicating specific areas where future improvements are needed.
- Single-Image vs Multi-Image Reasoning: Models perform notably better when images are provided separately rather than as a single image, especially for interleaved tasks. This suggests that the ability to process and reason with multiple discrete pieces of visual information is crucial.
- Failure Analysis: A detailed analysis reveals common failure modes such as calculation errors, confusion in similar elements, and misreading of visual information. This analysis provides valuable insights for targeted improvements in future models.
- Task Properties Impact: The paper also explores how different task properties affect model performance, indicating that current models have varying strengths and weaknesses depending on the task's nature (e.g., interleaved vs non-interleaved).
Implications and Future Directions
The introduction of ReMI has several significant implications:
- Enhanced Benchmarks: As multi-modal and multi-image reasoning capabilities become more critical, benchmarks like ReMI are essential for guiding the development and evaluation of LLMs.
- Model Improvement: The identified performance gaps and failure modes provide clear directions for future research. Models need better mechanisms for parsing and reasoning across multiple heterogeneous images, improved calculation accuracy, and more robust handling of sequential and set-based image consumption.
Conclusion
ReMI serves as a crucial benchmark for assessing and improving the multi-image reasoning capabilities of LLMs. The substantial gap between current model performance and human proficiency highlights the need for further advancements in this area. By covering a diverse array of reasoning tasks and key properties, ReMI lays a solid foundation for future research aimed at closing this gap and enhancing the multi-modal reasoning capabilities of LLMs.
Acknowledgements
The authors acknowledge Behnam Neyshabur for his invaluable feedback.
In summary, the paper makes a significant contribution by addressing the emerging need for specialized benchmarks tailored to multi-image reasoning and providing a comprehensive dataset that challenges the current state-of-the-art models, paving the way for future advancements in AI.