Friday, November 15, 2024

g-f(2)3184 Evaluating Generative Models: A New Study Reveals Surprising Limitations

 


Insights from Research


By Fernando Machuca and Gemini

Categorization:



Introduction:


The research paper, "Evaluating the World Model Implicit in a Generative Model," by Keyon Vafa, Jon Kleinberg, Justin Y. Chen, Ashesh Rambachan, and Sendhil Mullainathan investigates whether large language models (LLMs) can form coherent world models.  The authors propose new evaluation metrics inspired by the Myhill-Nerode theorem to assess the coherence of world models in various domains, including logical reasoning, geographic navigation, and game-playing.  Their findings reveal that while LLMs perform well on existing diagnostics, their world models are less coherent than they appear, leading to potential failures when faced with slightly different tasks.  The study emphasizes the need for improved metrics to evaluate the true understanding of LLMs and highlights the importance of building models that capture the underlying logic of the domains they model.  


Large language models (LLMs) have demonstrated remarkable capabilities in various domains, exceeding expectations based on their next-token prediction training objective.  This suggests that LLMs may be implicitly learning world models, which are representations of the underlying structure and rules governing the data they are trained on.  Evaluating the extent to which LLMs truly recover world models is crucial for understanding their potential and limitations.    



genioux GK Nugget:


"Evaluating whether generative models have successfully recovered world models requires going beyond simple next-token prediction and employing theoretically grounded metrics that assess the model's ability to compress and distinguish sequences based on underlying states." — Fernando Machuca and Gemini, November 15, 2024



genioux Foundational Fact:


The Myhill-Nerode theorem, a fundamental concept in language theory, highlights that distinct states within a system can be differentiated by specific sequences.  Inspired by this theorem, the authors propose two metrics: sequence compression, which evaluates whether the model recognizes that sequences leading to the same state should have the same continuations, and sequence distinction, which assesses whether the model correctly distinguishes sequences that lead to different states.    



The 10 Most Relevant genioux Facts:


  1. Generative models can perform well on existing diagnostics for world model recovery, such as next-token prediction tests and state probes, but still exhibit incoherence in their world models.    
  2. The sequence compression and distinction metrics are based on the Myhill-Nerode theorem, which states that distinct states in a deterministic finite automaton (DFA) can be differentiated by specific sequences.    
  3. The sequence compression metric evaluates whether a generative model recognizes that sequences leading to the same state should have the same continuations.    
  4. The sequence distinction metric assesses whether the model correctly distinguishes sequences that lead to different states.    
  5. In the context of taxi rides in New York City, transformers trained on turn-by-turn directions exhibit surprising route planning abilities but fail to recover the true street map, as revealed by the proposed metrics and graph reconstruction techniques.    
  6. The failure to recover the true world model leads to fragility in downstream tasks, such as route planning with detours.    
  7. Sequence models trained on Othello games can perform well on existing diagnostics but exhibit varying degrees of coherence in their world models depending on the training data (real vs. synthetic).    
  8. Large language models (LLMs) can solve logic puzzles with high accuracy but still exhibit incoherence in their world models when evaluated using the proposed metrics.    
  9. The proposed metrics are model-agnostic and can be applied to any generative model that operates on sequences, including transformers and LLMs.    
  10. The study highlights the importance of using theoretically grounded evaluation metrics to assess the true capabilities of generative models and their ability to recover world models.    



Conclusion:


The paper's findings underscore the importance of using rigorous evaluation metrics to assess the true capabilities of generative models and their ability to recover world models.  The proposed sequence compression and distinction metrics offer a valuable tool for identifying incoherence in world models that may not be captured by existing diagnostics.  This research contributes to a deeper understanding of how generative models learn and represent the underlying structure of the data they are trained on, paving the way for the development of more robust and reliable AI systems.    



g-f(2)3184: The Juice of Golden Knowledge


Concentrated wisdom for immediate application


"Generative models, despite impressive performance on tasks like next-token prediction, can have incoherent world models.  To assess their true capabilities, go beyond simple tests and use theoretically grounded metrics like sequence compression and distinction, inspired by the Myhill-Nerode theorem.  These metrics evaluate the model's ability to recognize that sequences leading to the same state should have the same continuations and to correctly distinguish sequences leading to different states.  This is crucial for building more robust AI systems that accurately capture the logic of the domains they model."  — Fernando Machuca and Gemini, November 15, 2024



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ABOUT THE AUTHORS



Keyon Vafa is a postdoctoral fellow at Harvard University, part of the Harvard Data Science Initiative. His research focuses on developing machine learning methods to address economic questions and leveraging insights from behavioral sciences to enhance machine learning techniques. Vafa completed his Ph.D. in Computer Science at Columbia University in 2023, where he was advised by David Blei. During his doctoral studies, he was recognized as an NSF GRFP Fellow and Cheung-Kong Innovation Doctoral Fellow. Vafa's work has been published in prestigious conferences and journals, and he has received accolades such as the Morton B. Friedman Memorial Prize for excellence in engineering. Before his doctoral studies, he was an undergraduate at Harvard, concentrating in computer science and statistics.



Justin Y. Chen is a PhD candidate in the Department of Electrical Engineering and Computer Science at MIT, focusing on the intersection of algorithms, machine learning, and data analysis. His research is particularly notable in the area of learning-augmented algorithms, which aim to improve the efficiency and speed of fundamental graph algorithms. Chen has made significant contributions to problems such as counting triangles in data streams, online bipartite matching, and differentially private computation of shortest graph paths. His innovative work has potential applications in various fields, from Google's ad market to kidney exchange programs. Before his doctoral studies, Justin completed his undergraduate degree in Computer Science at Stanford University.



Ashesh Rambachan is an Assistant Professor of Economics at MIT, where he focuses on the intersection of econometrics and machine learning. His research primarily explores the applications of machine learning in economics and causal inference, with a particular interest in algorithmic tools that drive decision-making in areas such as the criminal justice system and consumer lending markets. Rambachan completed his Ph.D. in Economics at Harvard University in 2022, where he was recognized as a National Science Foundation Graduate Research Fellow. He also holds an A.B. in Economics, Summa Cum Laude, from Princeton University. Before joining MIT, he was a Postdoctoral Researcher at Microsoft Research New England.



Jon Kleinberg is the Tisch University Professor in the Computer Science Department at Cornell University. His research focuses on the interface of networks and information, particularly the social and information networks that underpin the Web and other online media. Kleinberg is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences. He has received numerous awards, including the MacArthur Fellowship, the Nevanlinna Prize, and the ACM-Infosys Foundation Award in the Computing Sciences. Kleinberg is also known for his work on the HITS algorithm, which played a significant role in the development of web search technologies.



Sendhil Mullainathan is a Distinguished Professor in the MIT Department of Economics and the MIT Electrical Engineering & Computer Science (EECS) Department. His research bridges economics, behavioral science, and machine learning, focusing on complex problems in human behavior, social policy, and medicine. Mullainathan is known for his work on the impact of poverty on mental bandwidth, discrimination, and CEO pay, among other topics. He is a co-founder of Ideas42, a non-profit applying behavioral science to social issues, and J-PAL, the MIT Poverty Action Lab. Mullainathan has received numerous accolades, including a MacArthur Fellowship, and has held positions at Harvard and the University of Chicago Booth School of Business. His influential book, "Scarcity: Why Having Too Little Means So Much," co-authored with Eldar Shafir, has been widely recognized.



Classical Summary of the Research Paper:


The research paper "Evaluating the World Model Implicit in a Generative Model"  explores the limitations of generative models in recovering world models, even when they perform well on standard tasks.  The authors, Vafa, Kleinberg, Chen, Rambachan, and Mullainathan, argue for the importance of using theoretically grounded metrics to assess the true capabilities of generative models.    


The paper highlights that existing diagnostics, such as next-token prediction tests and state probes, may not fully capture the incoherence in world models learned by generative models.  To address this, the authors propose two new metrics based on the Myhill-Nerode theorem: sequence compression and sequence distinction.  These metrics evaluate the model's ability to compress sequences that lead to the same state and distinguish sequences that lead to different states.    


Through experiments on taxi rides in New York City, Othello games, and logic puzzles, the authors demonstrate that generative models can exhibit surprising limitations in recovering coherent world models, even when they perform well on specific tasks.  This incoherence can lead to fragility in downstream tasks, such as route planning with detours.    


The authors conclude by emphasizing the importance of using rigorous evaluation metrics to assess the true capabilities of generative models and their ability to recover world models.  This research contributes to a deeper understanding of how generative models learn and represent the underlying structure of data, paving the way for the development of more robust and reliable AI systems.   



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The genioux facts program has established a robust foundation of over 3183 Big Picture of the Digital Age posts [g-f(2)1 - g-f(2)3183].



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