125 research outputs found

    Degrees of Computability and Randomness

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    Conceptual Model Interpreter for Large Language Models

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    Large Language Models (LLMs) recently demonstrated capabilities for generating source code in common programming languages. Additionally, commercial products such as ChatGPT 4 started to provide code interpreters, allowing for the automatic execution of generated code fragments, instant feedback, and the possibility to develop and refine in a conversational fashion. With an exploratory research approach, this paper applies code generation and interpretation to conceptual models. The concept and prototype of a conceptual model interpreter is explored, capable of rendering visual models generated in textual syntax by state-of-the-art LLMs such as Llama~2 and ChatGPT 4. In particular, these LLMs can generate textual syntax for the PlantUML and Graphviz modeling software that is automatically rendered within a conversational user interface. The first result is an architecture describing the components necessary to interact with interpreters and LLMs through APIs or locally, providing support for many commercial and open source LLMs and interpreters. Secondly, experimental results for models generated with ChatGPT 4 and Llama 2 are discussed in two cases covering UML and, on an instance level, graphs created from custom data. The results indicate the possibility of modeling iteratively in a conversational fashion.Comment: ER Forum 2023, 42nd International Conference on Conceptual Modeling (ER 2023), November 6-9, 2023, Lisbon, P

    A Theory of Emergent In-Context Learning as Implicit Structure Induction

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    Scaling large language models (LLMs) leads to an emergent capacity to learn in-context from example demonstrations. Despite progress, theoretical understanding of this phenomenon remains limited. We argue that in-context learning relies on recombination of compositional operations found in natural language data. We derive an information-theoretic bound showing how in-context learning abilities arise from generic next-token prediction when the pretraining distribution has sufficient amounts of compositional structure, under linguistically motivated assumptions. A second bound provides a theoretical justification for the empirical success of prompting LLMs to output intermediate steps towards an answer. To validate theoretical predictions, we introduce a controlled setup for inducing in-context learning; unlike previous approaches, it accounts for the compositional nature of language. Trained transformers can perform in-context learning for a range of tasks, in a manner consistent with the theoretical results. Mirroring real-world LLMs in a miniature setup, in-context learning emerges when scaling parameters and data, and models perform better when prompted to output intermediate steps. Probing shows that in-context learning is supported by a representation of the input's compositional structure. Taken together, these results provide a step towards theoretical understanding of emergent behavior in large language models

    Faith and Fate: Limits of Transformers on Compositionality

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    Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. This begs the question: Are these errors incidental, or do they signal more substantial limitations? In an attempt to demystify Transformers, we investigate the limits of these models across three representative compositional tasks -- multi-digit multiplication, logic grid puzzles, and a classic dynamic programming problem. These tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer. We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures. Our empirical findings suggest that Transformers solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching, without necessarily developing systematic problem-solving skills. To round off our empirical study, we provide theoretical arguments on abstract multi-step reasoning problems that highlight how Transformers' performance will rapidly decay with increased task complexity.Comment: 10 pages + appendix (21 pages

    Large Language Models for Software Engineering: Survey and Open Problems

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    This paper provides a survey of the emerging area of Large Language Models (LLMs) for Software Engineering (SE). It also sets out open research challenges for the application of LLMs to technical problems faced by software engineers. LLMs' emergent properties bring novelty and creativity with applications right across the spectrum of Software Engineering activities including coding, design, requirements, repair, refactoring, performance improvement, documentation and analytics. However, these very same emergent properties also pose significant technical challenges; we need techniques that can reliably weed out incorrect solutions, such as hallucinations. Our survey reveals the pivotal role that hybrid techniques (traditional SE plus LLMs) have to play in the development and deployment of reliable, efficient and effective LLM-based SE

    An Identification System for Head Mounted Displays

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    Personalized devices often require a form of user identification to provide customized performance and rudimentary privacy between a limited amount of users. Because of the personal nature of head mounted devices, the new and growing industry of head mounted displays requires a method to identify users to increase customizability and usability of such devices. This project introduces a system that accurately identifies users with common sensors included on head mounted displays. The proposed system records user blink behavior, head position and head movement and then uses high dimensional machine learning algorithms to identify users based on trends in their collected data. The system demonstrated over 98% accuracy, demonstrating its ability to identify users

    Some optimization problems in power system reliability analysis

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    This dissertation aims to address two optimization problems involving power system reliabilty analysis, namely multi-area power system adequacy planning and transformer maintenance optimization. A new simulation method for power system reliability evaluation is proposed. The proposed method provides reliability indexes and distributions which can be used for risk assessment. Several solution methods for the planning problem are also proposed. The first method employs sensitivity analysis with Monte Carlo simulation. The procedure is simple yet effective and can be used as a guideline to quantify effectiveness of additional capacity. The second method applies scenario analysis with a state-space decomposition approach called global decomposition. The algorithm requires less memory usage and converges with fewer stages of decomposition. A system reliability equation is derived that leads to the development of the third method using dynamic programming. The main contribution of the third method is the approximation of reliability equation. The fourth method is the stochastic programming framework. This method offers modeling flexibility. The implementation of the solution techniques is presented and discussed. Finally, a probabilistic maintenance model of the transformer is proposed where mathematical equations relating maintenance practice and equipment lifetime and cost are derived. The closed-form expressions insightfully explain how the transformer parameters relate to reliability. This mathematical model facilitates an optimum, cost-effective maintenance scheme for the transformer

    The Digital Humanities and Literary Studies

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    This book delivers an introduction and overview of developing intersections between digital methods and literary studies. The Digital Humanities and Literary Studies serves as a starting place for those who wish to learn more about the possibilities, and the limitations, of the oft-touted digital humanities in the literary space. The volume engages with the proponents of digital humanities and its detractors alike, aiming to offer a fair and balanced perspective on this controversial topic. The book combines a survey and background approach with original literary research and, therefore, straddles the divide between seasoned digital experts and interested newcomers

    The Digital Humanities and Literary Studies

    Get PDF
    This book delivers an introduction and overview of developing intersections between digital methods and literary studies. The Digital Humanities and Literary Studies serves as a starting place for those who wish to learn more about the possibilities, and the limitations, of the oft-touted digital humanities in the literary space. The volume engages with the proponents of digital humanities and its detractors alike, aiming to offer a fair and balanced perspective on this controversial topic. The book combines a survey and background approach with original literary research and, therefore, straddles the divide between seasoned digital experts and interested newcomers
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