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Reducing Hallucination in Structured Output Generation via Retrieval-Augmented Modeling


Core Concepts
Using Retrieval-Augmented Generation (RAG) to reduce hallucination and improve the quality of structured outputs, such as workflows, generated from natural language requirements.
Abstract
The authors describe how they employed RAG to improve the trustworthiness of structured outputs, specifically workflows, generated from natural language requirements in a commercial application. Workflows are represented as JSON documents with steps and logic elements. The key highlights are: Fine-tuning a retriever model to map natural language to existing workflow steps and database table names helps reduce hallucination, which is a key limitation of generative AI systems. Providing the retriever's suggestions as part of the input to the language model during training allows the model to copy relevant components, leading to better performance. Using RAG enables deploying a smaller language model (as small as 3B parameters) with a very small retriever (110M parameters) without loss in performance, making the system less resource-intensive. Evaluation on in-domain and out-of-domain datasets shows that the RAG-based system significantly reduces hallucination compared to using the language model alone. Error analysis reveals that the quality of the retriever's suggestions and the language model's understanding of the task semantics are crucial for generating high-quality structured outputs.
Stats
Workflows can contain up to hundreds of steps and dozens of database tables. The authors' in-domain dataset contains 2,867 training, 318 development, and 798 test samples, with 823 trigger steps and 556 table names. The authors also evaluate on 5 out-of-domain datasets, where the percentage of steps not seen in the training data ranges from 7% to 76%.
Quotes
"A current limitation of Generative AI (GenAI) is its propensity to hallucinate." "Retrieval-Augmented Generation (RAG) is a well-known method that can reduce hallucination and improve output quality, especially when generating the correct output requires access to external knowledge sources."

Deeper Inquiries

How can the retriever and language model be further integrated to better leverage their respective strengths and mitigate their weaknesses

To further integrate the retriever and language model effectively, several strategies can be employed. Firstly, implementing a feedback loop mechanism where the retriever provides initial suggestions to the language model, and the model can then refine these suggestions based on the context of the input query. This iterative process can help improve the quality of the suggestions provided by the retriever and enhance the overall generation process. Additionally, incorporating domain-specific knowledge into the retriever can help tailor the suggestions to the specific requirements of the task at hand. This can be achieved through pre-training the retriever on domain-specific data or utilizing domain-specific embeddings to enhance the retrieval process. Furthermore, exploring ensemble methods where multiple retrievers with different strengths are used in conjunction with the language model can provide a more comprehensive and robust approach to structured output generation. By leveraging the complementary strengths of each component, the system can mitigate individual weaknesses and improve overall performance.

What other structured output tasks could benefit from a similar Retrieval-Augmented Generation approach, and how would the implementation differ

Several structured output tasks could benefit from a Retrieval-Augmented Generation approach, such as text-to-SQL generation, code generation from natural language, and document summarization. In the case of text-to-SQL generation, the retriever can suggest relevant SQL query components based on the input natural language query, while the language model can generate the complete SQL query incorporating these suggestions. For code generation tasks, the retriever can provide relevant code snippets or function definitions, which the language model can then integrate into the final code output. Document summarization tasks can also benefit from this approach, where the retriever suggests key sentences or phrases from the input document, and the language model generates a concise summary based on this retrieved information. The implementation for each task would involve training the retriever on task-specific data and fine-tuning the language model in a RAG fashion to incorporate the retrieved information into the generation process.

Can the authors' insights on reducing hallucination be applied to open-ended text generation tasks, where the output is not constrained by a specific schema

The insights provided by the authors on reducing hallucination in structured output tasks can be applied to open-ended text generation tasks with certain adaptations. In open-ended text generation, where the output is not constrained by a specific schema, the focus would be on reducing the generation of irrelevant or nonsensical content. By integrating a retriever to provide contextually relevant information to the language model during generation, the model can produce more coherent and contextually appropriate text. The retriever can suggest relevant phrases, entities, or concepts based on the input text, guiding the language model to generate more accurate and contextually consistent output. Additionally, techniques such as fine-tuning the language model on domain-specific data and incorporating domain knowledge into the retrieval process can help improve the quality of generated text and reduce hallucination in open-ended text generation tasks.
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