With the increasing adoption of Large Language Models A production-ready, generalizable framework for LLMbased (LLMs) in enterprise settings, ensuring accurate and reliable QA systems built on Elasticsearch question-answering systems remains a critical challenge. A flexible hybrid retrieval mechanism combining dense Building upon our previous work on domain-specific and sparse search methods question answering about Adobe products (Sharma et al. A comprehensive evaluation framework for assessing 2024), which established a retrieval-aware framework with QA system performance self-supervised training, we now present a production-ready, Empirical analysis demonstrating the effectiveness of our generalizable architecture alongside a comprehensive evaluation approach across various metrics methodology. Our core contribution is a flexible, scalable framework built on Elasticsearch that can be adapted Through this work, we provide not only theoretical insights for any LLM-based question-answering system. This framework but also a practical, deployable solution for building reliable seamlessly integrates hybrid retrieval mechanisms, domain-specific question-answering systems that can combining dense and sparse search with boost matching, be adapted to various enterprise needs.

Question answering (QA) has become an important application in the advanced development of large language models. General pre-trained large language models for question-answering are not trained to properly understand the knowledge or terminology for a specific domain, such as finance, healthcare, education, and customer service for a product. To better cater to domain-specific understanding, we build an in-house question-answering system for Adobe products. We propose a novel framework to compile a large question-answer database and develop the approach for retrieval-aware finetuning of a Large Language model. We showcase that fine-tuning the retriever leads to major improvements in the final generation. Our overall approach reduces hallucinations during generation while keeping in context the latest retrieval information for contextual grounding.

Large Language Models (LLMs) have issues with document question answering (QA) in situations where the document is unable to fit in the small context length of an LLM. To overcome this issue, most existing works focus on retrieving the relevant context from the document, representing them as plain text. However, documents such as PDFs, web pages, and presentations are naturally structured with different pages, tables, sections, and so on. Representing such structured documents as plain text is incongruous with the user's mental model of these documents with rich structure. When a system has to query the document for context, this incongruity is brought to the fore, and seemingly trivial questions can trip up the QA system. To bridge this fundamental gap in handling structured documents, we propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content. Our experiments demonstrate the effectiveness of the proposed PDFTriage-augmented models across several classes of questions where existing retrieval-augmented LLMs fail. To facilitate further research on this fundamental problem, we release our benchmark dataset consisting of 900+ human-generated questions over 80 structured documents from 10 different categories of question types for document QA. Our code and datasets will be released soon on Github.