One of the primary driving forces contributing to the superior performance of
Large Language Models (LLMs) is the extensive availability of human-annotated
natural language data, which is used for alignment fine-tuning. This inspired
researchers to investigate self-training methods to mitigate the extensive
reliance on human annotations. However, the current success of self-training
has been primarily observed in natural language scenarios, rather than in the
increasingly important neural-symbolic scenarios. To this end, we propose an
environment-guided neural-symbolic self-training framework named ENVISIONS. It
aims to overcome two main challenges: (1) the scarcity of symbolic data, and
(2) the limited proficiency of LLMs in processing symbolic language. Extensive
evaluations conducted on three distinct domains demonstrate the effectiveness
of our approach. Additionally, we have conducted a comprehensive analysis to
uncover the factors contributing to ENVISIONS's success, thereby offering
valuable insights for future research in this area. Code will be available at
https://github.com/xufangzhi/ENVISIONS.

通过研究所提出的名为 ENVISIONS 的环境引导的神经符号自训练框架，我们克服了符号数据的稀缺性和大型语言模型处理符号语言的能力有限这两个主要挑战，从而在三个不同领域的广泛评估中展示了我们的方法的有效性，并进行了全面分析以揭示贡献到 ENVISIONS 成功的因素，为未来的研究提供了有价值的见解。

交互演化：用于大型语言模型的神经符号自训练框架

Interactive Evolution: A Neural-Symbolic Self-Training Framework For  Large Language Models

Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, while complex symbolic datasets
often exhibit a latent hierarchical structure, state-of-the-art methods
typically learn embeddings in Euclidean vector spaces, which do not account for
this property. For this purpose, we introduce a new approach for learning
hierarchical representations of symbolic data by embedding them into hyperbolic
space -- or more precisely into an n-dimensional Poincar\'e ball. Due to the
underlying hyperbolic geometry, this allows us to learn parsimonious
representations of symbolic data by simultaneously capturing hierarchy and
similarity. We introduce an efficient algorithm to learn the embeddings based
on Riemannian optimization and show experimentally that Poincar\'e embeddings
outperform Euclidean embeddings significantly on data with latent hierarchies,
both in terms of representation capacity and in terms of generalization
ability.

通过将符号数据嵌入超载空间（或更确切地说是 n 维庞加莱球）来学习符号数据的分层表示的方法，通过实验证明 Poincare 嵌入在具有潜在层次结构的数据上显着优于欧几里得嵌入，无论是在表示能力还是泛化能力方面。