Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing

Nov 16, 2023 ·

This article presents a method for addressing the challenges of natural language understanding (NLU) by introducing a novel approach based on graph neural networks (GNNs). The proposed GNN-based NLU model leverages syntactic and semantic information associated with words to process sentences in an efficient and effective manner. Moreover, the proposed model is able to identify salient features of a sentence and better capture its context for improved understanding.

The main contribution of the paper is an end-to-end tailored GNN architecture that captures both syntactic and semantic information from words so as to enable the model to accurately determine the meaning of a sentence. To this end, the authors propose four key building blocks: a graph encoder, a graph decoder, a syntactic embedder, and a semantic embedder.

The graph encoder is used to capture syntactic and semantic information from words by leveraging their contextual relationships. This includes utilizing graph convolutional layers, which can capture both local and global information, as well as employing attention mechanisms to further refine the representation. Additionally, the graph decoder utilizes these representations to generate an output label corresponding to the meaning of the sentence.

To capture syntactic information, the syntactic embedder employs a bidirectional long short-term memory (LSTM) network along with word2vec embeddings, while the semantic embedder uses a skip-gram model to obtain the contextual meaning of words. Finally, the authors propose an optimization approach that combines both the graph encoder and decoder into a single learning framework, thus enabling the proposed GNN-based NLU model to achieve superior performance.

In summary, this paper proposes a novel approach for natural language understanding based on graph neural networks. The proposed model incorporates syntactic and semantic information from words to enhance sentence understanding. Furthermore, the authors introduce an end-to-end tailored GNN architecture along with an optimization approach, which enables the model to obtain a higher accuracy in NLU tasks.