MemoryGraph: uma proposta de memória para agentes conversacionais utilizando grafo de conhecimento
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2024-09-25
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Universidade Federal de Goiás
Resumo
With the advancement of massive language models applied to natural language
processing, many proposals have become viable, such as the use of conversational
agents applied to various everyday tasks. However, these models still have limitations
in both the integration of new knowledge and the representation and retrieval of that
knowledge, being constrained by costs, execution time, and training. Furthermore,
their black-box nature prevents the direct manipulation of knowledge, mainly due to
the vector representation that indirectly represents it, making the control and
explanation of their results more difficult. In contrast, knowledge graphs allow for a rich
and explicit representation of relationships between real-world entities. Despite the
challenges in their construction, studies indicate that these can complement each other
to produce better results. Therefore, the objective of this research is to propose a
memory system for conversational agents based on massive language models through
the combination of explicit knowledge (knowledge graphs) and implicit knowledge
(language models) to achieve better semantic and lexical representation. This
methodology was called MemoryGraph and is composed of three processes: graph
construction, graph search, and user representation. Various knowledge graph
construction workflows were proposed and compared, considering their costs and
influences on the final result. The agent can search for information in this base through
various search proposals based on RAG, referred to here as GraphRAG. This search
methodology was evaluated by humans in five proposed question scenarios, showing
superior average results in all five proposed search approaches (29% in the best
approach). In addition, six RAG metrics, evaluated by a massive model, were applied
to the proposed application results from two popular datasets and one composed of
diabetes guidelines, showing superior results in all datasets. Furthermore, a method
for long-term user representation, called user_memory, was proposed, demonstrating
93% retention of user information. To reinforce this result, case studies were
conducted, demonstrating the agent's ability to personalize the user experience based
on past experiences, increasing the speed of information delivery and user satisfaction.
The results demonstrate that the MemoryGraph paradigm represents an advance over
vector representation in environments where richer, temporal, and mutable
contextualization is necessary. It also indicates that the integration of knowledge
graphs with massive language models, especially in the construction of long-term
memory and rich contextualization based on past experiences, can represent a
significant advance in creating more efficient, personalized conversational agents with
enhanced capacity for retaining and utilizing information over time.
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Citação
OLIVEIRA, V. P. L. MemoryGraph: uma proposta de memória para agentes conversacionais utilizando grafo de conhecimento. 2024. 182 f. Tese (Doutorado em Ciência da Computação) - Instituto de Informática, Universidade Federal de Goiás, Goiânia, 2024.