Instruction-tuned large language models (LLMs) are capable of generating
stories in response to open-ended user requests, but the resulting stories tend
to be limited in their diversity. Older, symbolic approaches to story
generation (such as planning) can generate substantially more diverse plot
outlines, but are limited to producing stories that recombine a fixed set of
hand-engineered character action templates. Can we combine the strengths of
these approaches while mitigating their weaknesses? We propose to do so by
using a higher-level and more abstract symbolic specification of high-level
story structure -- implemented via answer set programming (ASP) -- to guide and
diversify LLM-based story generation. Via semantic similarity analysis, we
demonstrate that our approach produces more diverse stories than an unguided
LLM, and via code excerpts, we demonstrate the improved compactness and
flexibility of ASP-based outline generation over full-fledged narrative
planning.

使用回答集编程（ASP）的高层次和抽象符号规范引导和多样化基于 LLM 的故事生成，产生比未引导的 LLM 更多样化的故事，并通过代码摘录证明 ASP-based outline generation 相较于完整的叙事规划具有改进的紧凑性和灵活性。

借助解答集编程指导和多样化基于 LLM 的故事生成

Guiding and Diversifying LLM-Based Story Generation via Answer Set  Programming

The development of artificial intelligence systems with advanced reasoning
capabilities represents a persistent and long-standing research question.
Traditionally, the primary strategy to address this challenge involved the
adoption of symbolic approaches, where knowledge was explicitly represented by
means of symbols and explicitly programmed rules. However, with the advent of
machine learning, there has been a paradigm shift towards systems that can
autonomously learn from data, requiring minimal human guidance. In light of
this shift, in latest years, there has been increasing interest and efforts at
endowing neural networks with the ability to reason, bridging the gap between
data-driven learning and logical reasoning. Within this context, Neural
Algorithmic Reasoning (NAR) stands out as a promising research field, aiming to
integrate the structured and rule-based reasoning of algorithms with the
adaptive learning capabilities of neural networks, typically by tasking neural
models to mimic classical algorithms. In this dissertation, we provide
theoretical and practical contributions to this area of research. We explore
the connections between neural networks and tropical algebra, deriving powerful
architectures that are aligned with algorithm execution. Furthermore, we
discuss and show the ability of such neural reasoners to learn and manipulate
complex algorithmic and combinatorial optimization concepts, such as the
principle of strong duality. Finally, in our empirical efforts, we validate the
real-world utility of NAR networks across different practical scenarios. This
includes tasks as diverse as planning problems, large-scale edge classification
tasks and the learning of polynomial-time approximate algorithms for NP-hard
combinatorial problems. Through this exploration, we aim to showcase the
potential integrating algorithmic reasoning in machine learning models.

通过与神经网络和热带代数的连接，将算法推理集成到机器学习模型中的潜力展示出来。

图神经网络中的算法推理

Reasoning Algorithmically in Graph Neural Networks

The advancement of machine learning and symbolic approaches have underscored
their strengths and weaknesses in Natural Language Processing (NLP). While
machine learning approaches are powerful in identifying patterns in data, they
often fall short in learning commonsense and the factual knowledge required for
the NLP tasks. Meanwhile, the symbolic methods excel in representing
knowledge-rich data. However, they struggle to adapt dynamic data and
generalize the knowledge. Bridging these two paradigms through hybrid
approaches enables the alleviation of weaknesses in both while preserving their
strengths. Recent studies extol the virtues of this union, showcasing promising
results in a wide range of NLP tasks. In this paper, we present an overview of
hybrid approaches used for NLP. Specifically, we delve into the
state-of-the-art hybrid approaches used for a broad spectrum of NLP tasks
requiring natural language understanding, generation, and reasoning.
Furthermore, we discuss the existing resources available for hybrid approaches
for NLP along with the challenges, offering a roadmap for future directions.

综述了机器学习和符号方法在自然语言处理中的缺点和优势以及其通过混合方法的桥梁作用，讨论了混合方法在需要自然语言理解、生成和推理的广泛领域的最新研究，同时探讨了现有资源和面临的挑战，并提出了未来方向的路线图。

机器学习与符号方法的协同：自然语言处理中混合方法的综述

Synergizing Machine Learning & Symbolic Methods: A Survey on Hybrid  Approaches to Natural Language Processing

We describe basic ideas underlying research to build and understand
artificially intelligent systems: from symbolic approaches via statistical
learning to interventional models relying on concepts of causality. Some of the
hard open problems of machine learning and AI are intrinsically related to
causality, and progress may require advances in our understanding of how to
model and infer causality from data.

该研究阐述了建立和理解人工智能系统的基本思想：从符号方法到统计学习，再到基于因果关系概念的干预模型，其中一些机器学习和人工智能的难题内在地与因果关系有关，而进展可能需要我们加深对如何从数据中建模和推断因果关系的理解。