Aligning Large Language Models to a Domain-specific Graph Database

Graph Databases (Graph DB) are widely applied in various fields, including finance, social networks, and medicine. However, translating Natural Language (NL) into the Graph Query Language (GQL), commonly known as NL2GQL, proves to be challenging due to its inherent complexity and specialized nature. Some approaches have sought to utilize Large Language Models (LLMs) to address analogous tasks like text2SQL. Nevertheless, when it comes to NL2GQL taskson a particular domain, the absence of domain-specific NL-GQL data pairs makes it difficult to establish alignment between LLMs and the graph DB. To address this challenge, we propose a well-defined pipeline. Specifically, we utilize ChatGPT to create NL-GQL data pairs based on the given graph DB with self-instruct. Then, we use the created data to fine-tune LLMs, thereby achieving alignment between LLMs and the graph DB. Additionally, during inference, we propose a method that extracts relevant schema to the queried NL as the input context to guide LLMs for generating accurate GQLs.We evaluate our method on two constructed datasets deriving from graph DBs in finance domain and medicine domain, namely FinGQL and MediGQL. Experimental results demonstrate that our method significantly outperforms a set of baseline methods, with improvements of 5.90 and 6.36 absolute points on EM, and 6.00 and 7.09 absolute points on EX, respectively.

An LLM-Enhanced Adversarial Editing System for Lexical Simplification

Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to predict lexical edits in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced loss to enable the distillation of knowledge from Large Language Models (LLMs) into a small-size LS system. From that, complex words within sentences are masked and a Difficulty-aware Filling module is crafted to replace masked positions with simpler words. At last, extensive experimental results and analyses on three benchmark LS datasets demonstrate the effectiveness of our proposed method.