Advancing Large Language Models Exploration and Understanding through Visualization Techniques

Large Language Models (LLMs) have significantly impacted natural language processing tasks, enabling unprecedented performance across various applications. However, understanding the inner workings of these models remains a challenge due to their complex architectures and massive parameter sizes. Visualization techniques offer a promising avenue to unravel the black box of LLMs and provide insights into their behavior, decision-making processes, and internal representations. This proposal aims to explore advanced visualization methods to enhance the interpretability, transparency, and usability of LLMs.

Recent advancements in deep learning, particularly the emergence of transformer-based architectures like GPT (Generative Pre-trained Transformer), have led to the development of LLMs with billions of parameters. Despite their remarkable performance, concerns about their lack of interpretability, hallucinations and potential biases persist. Visualization techniques have been proposed as a means to address these issues by offering intuitive representations of complex model behavior and facilitating human understanding.

– Investigate existing visualization methods and frameworks for LLMs.
– Develop novel visualization techniques tailored to the unique characteristics of LLMs.
– Evaluate the effectiveness of visualization in improving model interpretability and trustworthiness.
– Explore applications of visualization for diagnosing biases, understanding attention mechanisms, and analyzing model responses to input stimuli.
– Investigate the impact of visualization on user interactions with LLMs in diverse domains.

– Systematic literature review on visualization techniques for deep learning models, with a focus on LLMs.
– Design and implementation of novel visualization methods, leveraging insights from cognitive science, human-computer interaction, and information visualization.
– Integrate visualization tools into existing LLM frameworks, ensuring compatibility and scalability.
– Conduct evaluation to assess efficacy of visualization in enhancing model interpretability, identifying biases, and aiding in decision-making processes.
– Collaborate with domain experts to apply visualization techniques to real-world LLM applications, such as language generation, summarization, and dialogue systems.

Expected Outcomes:
– A suite of advanced visualization techniques tailored to LLMs, including interactive tools for exploring model behavior.
– Insights into the inner workings of LLMs, enabling researchers to better understand model decisions and biases.
– Open-source software implementations of developed visualization methods, contributing to the wider research community.
– Publications in top-tier conferences and journals in the fields of natural language processing, machine learning, and visualization.
– Real-world applications demonstrating the utility of visualization for improving LLM interpretability and usability.

Year 1: Systematic literature review, familiarization with existing visualization techniques, and initial experimentation.
Year 2: Development and implementation of novel visualization methods, integration with LLM frameworks, and preliminary evaluation.
Year 3: Conduct comprehensive experiments, collaborate with domain experts, and refine visualization techniques based on feedback.
Year 4: Finalize research findings, write dissertation, and disseminate results through publications and presentations.

Haiyan Zhao, Hanjie Chen, Fan Yang, Ninghao Liu, Huiqi Deng, Hengyi Cai, Shuaiqiang Wang, Dawei Yin, and Mengnan Du. 2024. Explainability for Large Language Models: A Survey. ACM Trans. Intell. Syst. Technol. 15, 2, Article 20 (April 2024), 38 pages.

J. Choo and S. Liu, “Visual Analytics for Explainable Deep Learning,” in IEEE Computer Graphics and Applications, vol. 38, no. 4, pp. 84-92, Jul./Aug. 2018, doi: 10.1109/MCG.2018.042731661.

F. Hohman, M. Kahng, R. Pienta and D. H. Chau, “Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers,” in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 8, pp. 2674-2693, 1 Aug. 2019, doi: 10.1109/TVCG.2018.2843369.

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