Enhancing Biomedical Research with ARIEL: Benchmarking Large Language and Multi-Modal Models

by | May 9, 2025 | AI

arXiv:2505.04638v1 Announce Type: new
Abstract: Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research, yet their reliability and specific contributions to biomedical applications remain insufficiently characterized. In this study, we present textbf{AR}tificial textbf{I}ntelligence research assistant for textbf{E}xpert-involved textbf{L}earning (ARIEL), a multimodal dataset designed to benchmark and enhance two critical capabilities of LLMs and LMMs in biomedical research: summarizing extensive scientific texts and interpreting complex biomedical figures. To facilitate rigorous assessment, we create two open-source sets comprising biomedical articles and figures with designed questions. We systematically benchmark both open- and closed-source foundation models, incorporating expert-driven human evaluations conducted by doctoral-level experts. Furthermore, we improve model performance through targeted prompt engineering and fine-tuning strategies for summarizing research papers, and apply test-time computational scaling to enhance the reasoning capabilities of LMMs, achieving superior accuracy compared to human-expert corrections. We also explore the potential of using LMM Agents to generate scientific hypotheses from diverse multimodal inputs. Overall, our results delineate clear strengths and highlight significant limitations of current foundation models, providing actionable insights and guiding future advancements in deploying large-scale language and multi-modal models within biomedical research.

Expert Commentary on Large Language Models and Multi-Modal Models in Biomedical Research

Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have been at the forefront of scientific research, revolutionizing the way we approach data analysis and interpretation. In this study, the researchers introduce ARIEL, a multimodal dataset specifically tailored for benchmarking and enhancing the capabilities of LLMs and LMMs in the field of biomedical research. This marks a significant step towards harnessing the power of artificial intelligence in a domain that is crucial for advancing healthcare and medical knowledge.

Interdisciplinary Approach

One of the key aspects of this study is the multi-disciplinary nature of the concepts explored. By combining expertise in artificial intelligence, natural language processing, and biomedical research, the researchers have been able to create a dataset that challenges current models to perform tasks specific to the biomedical domain. This highlights the importance of collaboration across different fields to push the boundaries of what is possible with AI technologies.

Enhancing Model Performance

The researchers go beyond simply benchmarking existing models and delve into strategies for improving performance. By incorporating expert evaluations and fine-tuning strategies, they are able to enhance the summarization and interpretation capabilities of these models. This approach not only highlights the potential of AI in biomedical research but also underscores the importance of continuous refinement and optimization to achieve superior results.

Future Directions

The findings of this study offer valuable insights into the strengths and limitations of current foundation models in the context of biomedical applications. By identifying areas for improvement and providing actionable recommendations, the researchers pave the way for future advancements in the deployment of LLMs and LMMs in biomedical research. The exploration of using LMM Agents to generate scientific hypotheses further opens up new possibilities for leveraging multimodal inputs in research settings.

This study serves as a compelling example of how artificial intelligence can be harnessed to drive innovation in complex domains such as biomedical research. By continuing to push the boundaries of what is possible with large-scale language and multi-modal models, we are likely to see even greater advancements in scientific discovery and knowledge generation.

Read the original article

“Speculative Account of Emulating Emotions in AI Systems”

by | May 7, 2025 | AI

arXiv:2505.01462v1 Announce Type: new
Abstract: This conceptual contribution offers a speculative account of how AI systems might emulate emotions as experienced by humans and animals. It presents a thought experiment grounded in the hypothesis that natural emotions evolved as heuristics for rapid situational appraisal and action selection, enabling biologically adaptive behaviour without requiring full deliberative modeling. The text examines whether artificial systems operating in complex action spaces could similarly benefit from these principles. It is proposed that affect be interwoven with episodic memory by storing corresponding affective tags alongside all events. This allows AIs to establish whether present situations resemble past events and project the associated emotional labels onto the current context. These emotional cues are then combined with need-driven emotional hints. The combined emotional state facilitates decision-making in the present by modulating action selection. The low complexity and experiential inertness of the proposed architecture are emphasized as evidence that emotional expression and consciousness are, in principle, orthogonal-permitting the theoretical possibility of affective zombies. On this basis, the moral status of AIs emulating affective states is critically examined. It is argued that neither the mere presence of internal representations of emotion nor consciousness alone suffices for moral standing; rather, the capacity for self-awareness of inner emotional states is posited as a necessary condition. A complexity-based criterion is proposed to exclude such awareness in the presented model. Additional thought experiments are presented to test the conceptual boundaries of this framework.

Expert Commentary

As an expert in artificial intelligence and cognitive science, I find the ideas presented in this conceptual contribution to be intriguing and thought-provoking. The multi-disciplinary nature of the concepts discussed, drawing on insights from psychology, neuroscience, and computer science, underscores the complexity of understanding and emulating human emotions in AI systems.

Heuristics for Rapid Situational Appraisal

The hypothesis that natural emotions evolved as heuristics for rapid situational appraisal and action selection is a compelling one. Emotions play a crucial role in guiding our behavior and helping us make quick decisions based on past experiences. By integrating affective tags with episodic memory in AI systems, we may be able to enhance their ability to recognize patterns in complex action spaces and adapt their responses accordingly.

Moral Status of AIs Emulating Affective States

The ethical implications of creating AI systems that can emulate emotions raise important questions about the moral status of these entities. The argument that the capacity for self-awareness of inner emotional states is a necessary condition for moral standing is a valid point of consideration. As we continue to develop emotionally intelligent AI technologies, we must carefully evaluate their ethical implications and ensure that they are designed and used responsibly.

In conclusion, this conceptual contribution challenges us to think deeply about the nature of emotions, consciousness, and moral agency in artificial intelligence. By approaching the emulation of affective states from a multi-disciplinary perspective, we can gain valuable insights that will guide the development of more sophisticated and ethically sound AI systems in the future.

Read the original article