Enhancing Domain-Specific Question Answering with Retrieval-Augmented Generation for Adobe Products

Core Concepts

A novel framework to build an in-house question-answering system for Adobe products, leveraging retrieval-aware finetuning of a large language model and a smart retrieval system trained on Adobe data and user behavioral data.

Abstract

The paper presents a novel framework for building an in-house question-answering system for Adobe products. The key components are:

Retriever:

The retriever is trained using a large dataset of Adobe Helpx documents, Adobe Community questions, and generated QA pairs.
Contrastive learning is used to learn semantic representations for both queries and documents.
The retriever is trained using a weighted cross-entropy loss function based on user click data to prioritize more relevant documents.
The retrieval index is built using primary sources (Helpx documents, Community questions) and derived datasets (generated QA pairs).

Query Augmentation via Product Identification:

A product-intent extraction model is used to map queries to relevant Adobe products, improving retrieval and generation quality for ambiguous queries.

LLM Finetuning:

The large language model is finetuned using grounded documents, negative documents, and question-answer pairs.
Techniques like filtering short answers, using multiple positive and negative documents, and adding samples without grounded documents are used to improve the finetuned model.

Evaluation:

The retriever is evaluated using nDCG, showing significant improvements over open-source and commercial alternatives.
The full system is evaluated quantitatively and qualitatively, demonstrating its ability to provide accurate, up-to-date, and contextual answers for Adobe product-related questions.

The proposed framework addresses key challenges in domain-specific question answering, such as understanding product-specific terminology, keeping up with frequent product updates, and providing privacy-preserving solutions. The results showcase the effectiveness of the retrieval-augmented generation approach for enhancing question-answering performance on Adobe products.

Stats

The retrieval dataset contains 712,792 rows, with 180,799 unique queries and 22,576 unique documents.
The retrieval index database contains a total of 121,547 items, with 53.4% from Helpx articles, 12.5% from Community questions, 33.7% from generated Helpx QA pairs, and 0.4% from generated AdobeCare Video QA pairs.

Quotes

"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."
"Our overall approach reduces hallucinations during generation while keeping in context the latest retrieval information for contextual grounding."

Key Insights Distilled From

Retrieval Augmented Generation for Domain-specific Question Answering

by Sanat Sharma... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14760.pdf

Deeper Inquiries

How can the proposed framework be extended to other domains beyond Adobe products?

The proposed framework for retrieval-augmented generation can be extended to other domains by adapting the training data and model to the specific terminology, knowledge, and user behavior patterns of those domains. This extension would involve:

Data Collection: Gather domain-specific documents, user queries, and relevant information sources to create a comprehensive dataset for training the retriever and generator.
Retriever Training: Fine-tune the retriever model on the new domain-specific data to understand the context and retrieve relevant information effectively.
Generator Training: Train the generator model on the domain-specific QA pairs to ensure accurate and informative responses to user queries.
Product Disambiguation: Implement product identification models tailored to the new domain to handle ambiguous queries effectively.
Named Entity Removal: Customize the named entity removal module to cater to the privacy and data protection requirements of the new domain.

What are the potential challenges in scaling the retrieval-augmented generation approach to handle a broader range of user queries?

Scaling the retrieval-augmented generation approach to handle a broader range of user queries may face several challenges, including:

Data Diversity: Ensuring the availability of diverse and representative data across different domains to train the retriever and generator effectively.
Model Generalization: Ensuring that the models can generalize well to new domains without overfitting to specific datasets.
Product Disambiguation: Handling ambiguous queries that may refer to multiple products or concepts across various domains.
Privacy Concerns: Adhering to strict privacy regulations and ensuring the protection of sensitive information in different domains.
Model Performance: Maintaining high performance and accuracy while scaling to handle a larger volume of user queries and diverse topics.
User Personalization: Incorporating user preferences and past interactions to provide personalized responses across different domains.

How can the system be further improved to provide more personalized and contextual responses based on user preferences and past interactions?

To enhance the system for more personalized and contextual responses based on user preferences and past interactions, the following strategies can be implemented:

User Profiling: Develop user profiles based on past interactions, preferences, and behavior to tailor responses to individual users.
Contextual Understanding: Implement context-aware models that consider the user's history, current query, and context to generate relevant responses.
Feedback Loop: Incorporate a feedback mechanism where users can provide input on the relevance and quality of responses to continuously improve the system.
Dynamic Content Generation: Generate dynamic content based on real-time data, user behavior, and preferences to offer up-to-date and personalized information.
Multi-turn Conversations: Enable the system to engage in multi-turn conversations, remembering past interactions to maintain continuity and coherence in responses.
Preference Learning: Utilize machine learning algorithms to learn user preferences over time and adapt the responses accordingly for a more personalized experience.