## 2 Background and Related Works  
### Related Work  
Compared to research on Text2 SQL (Natural Query-to-SQL Query) [11, 12], studies on Text2 Cypher have emerged more recently. Similar to the early stages of Text2 SQL research, initial approaches to Text2 Cypher primarily relied on rule-based and template-based methods [5, 6]. These methods required manually crafted rules for each graph schema, making them inadequate for addressing the semantic gap between natural language and graph queries effectively. Several approaches have attempted to address the semantic gap between a natural query and a Cypher query, as in Text2 SQL. For example, the Intent-Based Natural Language Interface [5] employed a two-stage pipeline combining intent detection and named entity recognition (NER) with rule-based query construction. The first stage uses machine learning for intent detection and NER, while the second stage utilizes a rule-based component to construct the corresponding Cypher query. This approach highlights the utility of sentence semantics for rule-based conversion, derived from intent detection and NER. However, it faces limitations in handling entity linking and the logical structures of complex sentences, which can lead to incomplete or incorrect queries, especially for sentences with intricate relationships. The GraphQ IR [6] introduced an intermediate representation (IR) designed to bridge the gap between natural language queries and Cypher queries. This approach consists of a neural semantic parser (using BART [13]) to convert natural language into an intermediate representation, followed by a compiler that converts this IR into a graph query. While this method effectively addresses the semantic gap between natural and graph queries, it encounters challenges with entity disambiguation and limited support for complex logic. Additionally, since GraphQ IR is schema-specific, it struggles with cross-domain adaptability. Recent advances in LLM-based semantic parsing have significantly improved the translation of natural language into structured queries. For example, [PERSON]. [14] proposes an intelligent database query engine that leverages large language models to understand user intents and generate executable SQL queries, demonstrating the potential of LLMs in real-world semantic parsing tasks. Although these approaches are useful in controlled settings, such as rule-based systems and intermediate representations, they often perform best when the input follows a formal and grammatically correct structure. They tend to struggle with casual or less structured language, which is common in real-world scenarios. In contrast, large language models (LLMs) offer a more flexible solution since LLMs have been pretrained, instruction-tuned, and aligned with human preference on massive amounts of text datasets and have learned various language patterns, logical forms, and contextual structures. Thus, LLMs can interpret natural language in a generalizable way, including understanding and generating graph queries across different domains. Thus, LLM-based approaches become the mainstream of 'Text2 Query (SQL or Cypher)' research. [PERSON]. [15] developed a methodology that leverages LLMs to bridge the gap between textual questions and structured knowledge representations. Thus, it takes a natural language question and its NER results as input, combined with different prompts to generate a Cypher statement. Ref. [16] presents an integration of large language models into NoSQL databases and knowledge graphs to facilitate real-time translation of natural language into Cypher queries; it does not handle the improvement in Text2 Cypher task performance. Neo4j's Text2 Cypher [7], introduced in 2024, first constructed a comprehensive Text2 Cypher dataset consisting of 44,387 instances from diverse application domains, offering a rich resource for fine-tuning LLMs on the task of converting text to Cypher queries. This study also demonstrated that further fine-tuning baseline LMs using the Text2 Cypher dataset enhances performance.