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Adversarial inputs for humans differ from those for Language Models (LLMs), suggesting a divergence in what appears meaningful. This observation stems from deep theorization treating LLMs as dynamical systems using control theory, revealing the vast reachability space. Despite misconceptions on reduced reachability through techniques like fine-tuning, studies show the reachability space is more extensive than anticipated, manifesting through chaotic adversarial prompts.
Control theory provides a fresh perspective on optimizing large language models (LLMs) by focusing on feedback control principles applied to systems like engines. By considering controllability in LLMs, the emphasis shifts to understanding the controllable reachability space using control inputs. This approach aims to enhance the robustness and reliability of LLMs, delving into the dynamics and controllability mechanisms within these complex language systems.
Distinct from traditional control systems, Language Models (LLMs) present unique challenges due to their discrete token-based state space that expands with each generated token. The complexity arises from the exponential growth in possible trajectories, especially given the vast vocabulary size. This characteristic complicates control theory applications, highlighting the intricate nature of steering LLM outputs efficiently.
The research delves into prompt engineering and control theory interactions within Language Models (LLMs), indicating a shift towards a theoretical understanding of LLM dynamics. By investigating reachability sets and controllability metrics in LLMs, the study sheds light on optimizing prompt inputs for desired outputs. The findings underscore the importance of employing control theory to unravel the intricate dynamics of LLMs for enhanced control and performance.
The discussion transitions towards collective intelligence and the vision of decentralized artificial intelligence systems. Emphasizing the power of distributed small LLMs communicating through text prompts, the goal is to foster a collaborative intelligence system accessible to all. This approach aligns with creating information processing systems akin to biological paradigms like predictive coding, aiming for efficient and scalable AI applications.
Examining the intersection of biological neural models and control theory in artificial intelligence, the research explores the potential for robust and distributed AI systems. Leveraging insights from neural cellular automata and biological intelligence, the endeavor seeks to engineer collective intelligence systems resembling biological principles. This approach aims to optimize intelligence systems for scalability, robustness, and efficiency through a fusion of biological and control-theoretic methodologies.
In discussing model robustness, the focus shifts between enhancing model robustness directly versus augmenting it at the software layer. Evaluating the feasibility of bolstering language models' robustness through distinct approaches, the debate centers around fortifying controls at the software level to mitigate adversarial attacks and enhance system resilience. The discourse underscores the intricate balance between scaling model flexibility and curbing vulnerabilities, presenting essential considerations for advancing language model development and application.
The podcast delves into the concept of controlling language models through careful prompting. The discussion highlights the vast token space available, emphasizing the challenges of prompt engineering in eliciting specific outputs from language models. It references the self-attention control ability theorem, co-created by Shizuo Lui, which explains the importance of strong prompts in steering language models towards desired outputs. The theorem relates to self-attention mechanisms and their influence on the controllability and reachability of language models.
The episode introduces the Society for the Pursuit of AGI as a student organization fostering unconventional, innovative ideas in artificial general intelligence. It emphasizes the organization's interdisciplinary approach, aiming to explore diverse perspectives including behavioral economics, political science, and the arts for deeper insights into intelligence. The discussion reflects on the importance of conceptual breakthroughs in AI progress, highlighting the need for better understanding and collaboration across various fields to ensure the development of beneficial and ethically sound AI systems.
These two scientists have mapped out the insides or “reachable space” of a language model using control theory, what they discovered was extremely surprising.
Please support us on Patreon to get access to the private Discord server, bi-weekly calls, early access and ad-free listening.
https://patreon.com/mlst
YT version: https://youtu.be/Bpgloy1dDn0
Aman Bhargava from Caltech and Cameron Witkowski from the University of Toronto to discuss their groundbreaking paper, “What’s the Magic Word? A Control Theory of LLM Prompting.” (the main theorem on self-attention controllability was developed in collaboration with Dr. Shi-Zhuo Looi from Caltech).
They frame LLM systems as discrete stochastic dynamical systems. This means they look at LLMs in a structured way, similar to how we analyze control systems in engineering. They explore the “reachable set” of outputs for an LLM. Essentially, this is the range of possible outputs the model can generate from a given starting point when influenced by different prompts. The research highlights that prompt engineering, or optimizing the input tokens, can significantly influence LLM outputs. They show that even short prompts can drastically alter the likelihood of specific outputs. Aman and Cameron’s work might be a boon for understanding and improving LLMs. They suggest that a deeper exploration of control theory concepts could lead to more reliable and capable language models.
We dropped an additional, more technical video on the research on our Twitter account here: https://x.com/MLStreetTalk/status/1795093759471890606
Additional 20 minutes of unreleased footage on our Patreon here: https://www.patreon.com/posts/whats-magic-word-104922629
What's the Magic Word? A Control Theory of LLM Prompting (Aman Bhargava, Cameron Witkowski, Manav Shah, Matt Thomson)
https://arxiv.org/abs/2310.04444
LLM Control Theory Seminar (April 2024)
https://www.youtube.com/watch?v=9QtS9sVBFM0
Society for the pursuit of AGI (Cameron founded it)
https://agisociety.mydurable.com/
Roger Federer demo
http://conway.languagegame.io/inference
Neural Cellular Automata, Active Inference, and the Mystery of Biological Computation (Aman)
https://aman-bhargava.com/ai/neuro/neuromorphic/2024/03/25/nca-do-active-inference.html
Aman and Cameron also want to thank Dr. Shi-Zhuo Looi and Prof. Matt Thomson from from Caltech for help and advice on their research. (https://thomsonlab.caltech.edu/ and https://pma.caltech.edu/people/looi-shi-zhuo)
https://x.com/ABhargava2000
https://x.com/witkowski_cam
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