Long-term memory plays a critical role in personal interaction, considering
long-term memory can better leverage world knowledge, historical information,
and preferences in dialogues. Our research introduces PerLTQA, an innovative QA
dataset that combines semantic and episodic memories, including world
knowledge, profiles, social relationships, events, and dialogues. This dataset
is collected to investigate the use of personalized memories, focusing on
social interactions and events in the QA task. PerLTQA features two types of
memory and a comprehensive benchmark of 8,593 questions for 30 characters,
facilitating the exploration and application of personalized memories in Large
Language Models (LLMs). Based on PerLTQA, we propose a novel framework for
memory integration and generation, consisting of three main components: Memory
Classification, Memory Retrieval, and Memory Synthesis. We evaluate this
framework using five LLMs and three retrievers. Experimental results
demonstrate that BERT-based classification models significantly outperform LLMs
such as ChatGLM3 and ChatGPT in the memory classification task. Furthermore,
our study highlights the importance of effective memory integration in the QA
task.

该研究通过引入 PerLTQA 数据集，结合语义和经验记忆，包括世界知识、个人资料、社交关系、事件和对话，旨在调查个性化记忆在 QA 任务中的应用，强调了记忆整合在 QA 任务中的重要性。

PerLTQA: 一个个人长期记忆数据集，用于问题回答中的记忆分类、检索和合成

PerLTQA: A Personal Long-Term Memory Dataset for Memory Classification,  Retrieval, and Synthesis in Question Answering

Answering real-world tourism questions that seek Point-of-Interest (POI)
recommendations is challenging, as it requires both spatial and non-spatial
reasoning, over a large candidate pool. The traditional method of encoding each
pair of question and POI becomes inefficient when the number of candidates
increases, making it infeasible for real-world applications. To overcome this,
we propose treating the QA task as a dense vector retrieval problem, where we
encode questions and POIs separately and retrieve the most relevant POIs for a
question by utilizing embedding space similarity. We use pretrained language
models (PLMs) to encode textual information, and train a location encoder to
capture spatial information of POIs. Experiments on a real-world tourism QA
dataset demonstrate that our approach is effective, efficient, and outperforms
previous methods across all metrics. Enabled by the dense retrieval
architecture, we further build a global evaluation baseline, expanding the
search space by 20 times compared to previous work. We also explore several
factors that impact on the model's performance through follow-up experiments.
Our code and model are publicly available at this https URL

通过使用预训练语言模型和训练空间编码器，本研究提出了一种稠密检索体系结构来回答真实世界旅游问题，其中问题和兴趣点的相关性是通过嵌入空间相似性来确定的。实验结果表明，该方法在真实世界旅游问答数据集上表现出色，并且在所有指标上优于之前的方法。

基于位置感知的旅游问答模块化双编码器

Location Aware Modular Biencoder for Tourism Question Answering

Knowledge and expertise in the real-world can be disjointedly owned. To solve
a complex question, collaboration among experts is often called for. In this
paper, we propose CollabQA, a novel QA task in which several expert agents
coordinated by a moderator work together to answer questions that cannot be
answered with any single agent alone. We make a synthetic dataset of a large
knowledge graph that can be distributed to experts. We define the process to
form a complex question from ground truth reasoning path, neural network agent
models that can learn to solve the task, and evaluation metrics to check the
performance. We show that the problem can be challenging without introducing
prior of the collaboration structure, unless experts are perfect and uniform.
Based on this experience, we elaborate extensions needed to approach
collaboration tasks in real-world settings.

提出了一种新的具有复杂度的问题，并由多个专家代理协调工作，利用知识图谱和神经网络来解决问题，并说明了解决复杂问题协作结构的先前和后续扩展的必要性。