Significant strides have been made in natural language processing (NLP) using large language models (LLMs). However, LLMs struggle with structured information, leading to a need for new approaches. A team introduced StructLM, surpassing task-specific models on 14 of 18 datasets and achieving new state-of-the-art results. Despite progress, they recognize the need for broader dataset diversity.
Advancing Large Language Models for Structured Knowledge Grounding with StructLM: Model Based on CodeLlama Architecture
Introduction
Significant progress has been made in natural language processing (NLP) through large language models (LLMs). However, these models often struggle with structured information, revealing a notable gap in their capabilities. This highlights the need for innovative approaches to enhance LLMs’ structured knowledge grounding (SKG) capabilities.
Practical Solutions
Various methods have been developed to address SKG tasks, including learning contextual representations of tabular data, integrating relation-aware self-attention, and conducting pretraining over tabular/database data. Recent advancements have focused on unifying SKG tasks into a sequence-to-sequence format and using prompting frameworks on powerful LLMs for more robust and accurate task-solving. Instruction-tuning (IT) has been used to enhance the controllability and predictability of LLMs, aligning them with user expectations and improving downstream task performance.
A team of researchers has introduced StructLM, a novel model designed to bridge the gap in SKG capabilities. Leveraging a comprehensive instruction tuning dataset, StructLM surpasses task-specific models across a spectrum of datasets. The model shows strong generalization performance, outperforming existing models in grounding structured and unstructured knowledge.
Value
The development of StructLM is a major advancement in improving the SKG capabilities of LLMs. It establishes new benchmarks across multiple SKG tasks and demonstrates strong generalization performance, highlighting its potential to redefine LLMs’ structured data interpretation landscape.
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