😮 The Downsides of Structured Outputs

And how to design better Graph RAG systems for your domain knowledge

Pascal Biese

Aug 09, 2024

The downsides of structured outputs
From chaining thoughts to thinking on graphs
Graph RAG for domain knowledge

1. Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

Watching: Structured Outputs (paper)

What problem does it solve? Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, but their application in real-world scenarios often requires the output to be in structured formats like JSON or XML. This is particularly important for extracting key information from the generated content and facilitating seamless integration with other systems. However, the impact of imposing such structural constraints on the reasoning abilities and domain knowledge comprehension of LLMs has not been thoroughly investigated.

How does it solve the problem? This study addresses the problem by conducting a comprehensive evaluation of LLMs' performance when generating structured output compared to free-form responses across various common tasks. By systematically varying the strictness of format constraints, the researchers aim to quantify the extent to which these restrictions affect the models' reasoning capabilities. The findings suggest that enforcing structured output formats can lead to a significant decline in LLMs' reasoning abilities, with stricter constraints resulting in greater performance degradation.

What's next? The insights gained from this study highlight the need for further research into developing techniques that can maintain the reasoning abilities of LLMs while generating structured output. Potential avenues for future work include exploring novel architectures or training strategies that can better balance the trade-off between format adherence and reasoning performance. Additionally, investigating the underlying causes of the observed performance decline and developing methods to mitigate these issues could lead to more effective deployment of LLMs in real-world applications requiring structured generation.

2. Think-on-Graph 2.0: Deep and Interpretable Large Language Model Reasoning with Knowledge Graph-guided Retrieval

Watching: Think-on-Graph 2.0 (paper)

What problem does it solve? Large Language Models (LLMs) have shown impressive capabilities in generating coherent and fluent text. However, they often struggle with complex reasoning tasks and maintaining consistency across diverse queries. This is partly due to their reliance on the knowledge acquired during pretraining, which can be incomplete or outdated. Retrieval-augmented generation (RAG) aims to address these limitations by enabling LLMs to dynamically retrieve relevant information from external sources during inference.

How does it solve the problem? Think-on-Graph 2.0 (ToG2.0) enhances the RAG paradigm by leveraging knowledge graphs (KGs) to guide the retrieval process. Instead of simply retrieving relevant documents, ToG2.0 aligns the input questions with the KG and uses it as a navigational tool. This approach allows the model to make deep and long-range associations, ensuring logical consistency and optimizing the scope of retrieval. By incorporating semantic similarity guided by precise directives, ToG2.0 also improves the factual consistency of the generated responses.

What's next? The success of ToG2.0 highlights the potential of hybrid structured knowledge systems in advancing LLM reasoning capabilities. By combining the strengths of LLMs and structured knowledge bases, such as KGs, we can develop models that exhibit more human-like performance in complex reasoning tasks. Future research could explore the integration of other types of structured knowledge, such as ontologies or rule-based systems, to further enhance the reasoning abilities of LLMs. Additionally, the development of more efficient retrieval mechanisms and the incorporation of real-time updates to the knowledge base could make these hybrid systems more practical for real-world applications.

3. Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generation

Watching: MedGraphRAG (paper)

What problem does it solve? Large Language Models (LLMs) have shown impressive capabilities in various domains, including healthcare. However, when it comes to handling sensitive medical data, generating reliable and evidence-based responses is crucial. Traditional methods often fall short in capturing the full context of medical information, leading to potential inaccuracies and safety concerns.

How does it solve the problem? MedGraphRAG introduces a novel graph-based Retrieval-Augmented Generation (RAG) framework tailored for the medical domain. It starts by employing a hybrid static-semantic approach for document chunking, which significantly improves context capture compared to traditional methods. Extracted entities are then used to construct a three-tier hierarchical graph structure, connecting entities to foundational medical knowledge from papers and dictionaries. These entities are further linked to form meta-graphs, which are merged based on semantic similarities to create a comprehensive global graph. This structured representation enables precise information retrieval and evidence-based response generation. The retrieval process utilizes a U-retrieve method to balance global awareness and indexing efficiency of the LLM.

What's next? The MedGraphRAG framework has demonstrated its effectiveness through a comprehensive ablation study, consistently outperforming state-of-the-art models on multiple medical Q&A benchmarks. Moreover, the generated responses include source documentation, enhancing the reliability and trustworthiness of medical LLMs in practical applications. As the demand for accurate and safe medical information grows, frameworks like MedGraphRAG are poised to play a crucial role in advancing the use of LLMs in healthcare. Additionally, exploring the applicability of similar approaches to other specialized domains, such as legal or financial services, could unlock new possibilities for reliable and evidence-based LLM applications.

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