There has recently been growing interest in conversational agents with
long-term memory which has led to the rapid development of language models that
use retrieval-augmented generation (RAG). Until recently, most work on RAG has
focused on information retrieval from large databases of texts, like Wikipedia,
rather than information from long-form conversations. In this paper, we argue
that effective retrieval from long-form conversational data faces two unique
problems compared to static database retrieval: 1) time/event-based queries,
which requires the model to retrieve information about previous conversations
based on time or the order of a conversational event (e.g., the third
conversation on Tuesday), and 2) ambiguous queries that require surrounding
conversational context to understand. To better develop RAG-based agents that
can deal with these challenges, we generate a new dataset of ambiguous and
time-based questions that build upon a recent dataset of long-form, simulated
conversations, and demonstrate that standard RAG based approaches handle such
questions poorly. We then develop a novel retrieval model which combines
chained-of-table search methods, standard vector-database retrieval, and a
prompting method to disambiguate queries, and demonstrate that this approach
substantially improves over current methods at solving these tasks. We believe
that this new dataset and more advanced RAG agent can act as a key benchmark
and stepping stone towards effective memory augmented conversational agents
that can be used in a wide variety of AI applications.

近年来对长期记忆对话代理的兴趣越来越浓，这导致了使用检索增强生成（RAG）的语言模型的快速发展。本论文主张，在从长对话数据中进行有效的检索面临两个独特的问题：1）基于时间 / 事件的查询，要求模型根据时间或对话事件的顺序（例如，星期二的第三次对话）检索先前对话的信息；2）需要周围对话上下文才能理解的模糊查询。为了更好地开发能够应对这些挑战的 RAG 代理，我们生成了一个包含模糊查询和基于时间的问题的新数据集，该数据集是基于最新的长形模拟对话数据集的。我们证明了标准的 RAG 方法在处理这些问题时效果不佳。然后，我们开发了一种新的检索模型，结合了链式搜索方法、标准向量数据库检索和提示方法以消除查询的歧义，并证明了这种方法在解决这些任务上显著改善了现有方法的效果。我们相信，这个新数据集和更先进的 RAG 代理可以成为有效的记忆增强对话代理的关键基准和垫脚石，可用于各种人工智能应用。