The paper describes a system that uses large language model (LLM) technology
to support the automatic learning of new entries in an intelligent agent's
semantic lexicon. The process is bootstrapped by an existing non-toy lexicon
and a natural language generator that converts formal, ontologically-grounded
representations of meaning into natural language sentences. The learning method
involves a sequence of LLM requests and includes an automatic quality control
step. To date, this learning method has been applied to learning multiword
expressions whose meanings are equivalent to those of transitive verbs in the
agent's lexicon. The experiment demonstrates the benefits of a hybrid learning
architecture that integrates knowledge-based methods and resources with both
traditional data analytics and LLMs.

该论文介绍了一种利用大型语言模型技术支持智能代理语义词典中新条目的自动学习的系统。这种学习方法通过现有的非玩具词典和自然语言生成器引导启动，将意义的表达按基本本体建立转换为自然语言句子。此学习方法已应用于学习多词表达式，其含义与智能代理语义词典中的及物动词等效。实验展示了一种融合基于知识的方法、资源、传统数据分析和大型语言模型的混合学习架构的优势。

利用 LLMs 实现内容为中心的认知代理的知识获取自动化

Automating Knowledge Acquisition for Content-Centric Cognitive Agents  Using LLMs

Pedestrian behavior prediction is one of the major challenges for intelligent
driving systems in urban environments. Pedestrians often exhibit a wide range
of behaviors and adequate interpretations of those depend on various sources of
information such as pedestrian appearance, states of other road users, the
environment layout, etc. To address this problem, we propose a novel
multi-modal prediction algorithm that incorporates different sources of
information captured from the environment to predict future crossing actions of
pedestrians. The proposed model benefits from a hybrid learning architecture
consisting of feedforward and recurrent networks for analyzing visual features
of the environment and dynamics of the scene. Using the existing 2D pedestrian
behavior benchmarks and a newly annotated 3D driving dataset, we show that our
proposed model achieves state-of-the-art performance in pedestrian crossing
prediction.

本研究提出了一种新颖的多模态预测算法，采用混合学习架构来分析环境的视觉特征和场景动态，预测行人的未来横穿行为，并在现有 2D 行人行为基准和新的 3D 驾驶数据集上进行评估，取得了最先进的表现。