Published on 00/00/0000
Last updated on 00/00/0000
Published on 00/00/0000
Last updated on 00/00/0000
Share
INSIGHTS
6 min read
Published on 07/11/2024
Last updated on 07/17/2024
The benefits of retrieval-augmented generation for knowledge-intensive NLP tasks
Share
Natural language processing (NLP), a subset of artificial intelligence (AI), gives computers the ability to understand and interpret meaning within language. It’s used for various applications, from analyzing customer feedback to translating documents and generating marketing content. Advancements in NLP have also enabled the proliferation of large language models (LLMs), AI tools that are highly effective at generating human-like responses to text queries.
Despite the impressive functionality of NLP-based technologies like LLMs, even sophisticated, parameter-abundant models often underperform on specialized tasks. This is due to three main NLP limitations:
- NLP systems are limited to the knowledge they acquire over a finite training period, so their accuracy may decrease as training data becomes outdated.
- Models cannot deliver reliable results when prompted on tasks beyond the scope of their foundational knowledge.
- Models tend to improvise or hallucinate, producing false or irrelevant information due to knowledge gaps.
To address these limitations and maintain the accuracy and relevance of AI-generated content, enterprises can adopt retrieval-augmented generation (RAG).
How RAG works
RAG enhances AI outputs by enabling models to retrieve additional information from a source external to the model. When implementing RAG, developers build an external knowledge base containing factual, organized, and up-to-date information on a target task or domain. Then, they build a “retriever,” a mechanism the model uses to reference knowledge base data before responding to prompts. Techniques like keyword matching and semantic similarity searches allow the model to zero in on relevant context in the new dataset.
With RAG, models can incorporate new knowledge into their reasoning processes, even if this knowledge wasn’t available in the initial training data. However, unlike fine-tuning, which updates the model itself, RAG leaves the original architecture intact and simply stores the new information externally.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Pairing RAG and NLP can bolster AI output reliability, particularly for domain-intensive tasks. The two technologies have complementary strengths, combining the factuality of RAG with the ingenuity of NLP-based content generators. RAG helps NLP systems capture more context-rich and accurate responses for queries requiring specialized knowledge—such as technical support or legal guidance.
RAG is critical for some AI-supported tasks, particularly those where accuracy is paramount. For example, NLP systems trained to make medical diagnoses or treatment decisions could pose serious public health risks without access to an updated knowledge base with current domain expertise.
For businesses, RAG also offers benefits such as cost savings, efficiency, and adaptability. Developers can update knowledge bases as needed with relative ease compared to fine-tuning, which requires training a model on new data to keep outputs relevant. RAG is generally faster to implement than fine-tuning and demands significantly fewer compute resources. This is ideal for reducing costs and improving speed-to-market for NLP-based solutions.
Beyond ensuring output accuracy, the structured and logical nature of knowledge base retrieval means developers can trace how a model generated a given output. This level of transparency is difficult to achieve without RAG because NLP systems tend to use opaque algorithms that obscure their internal reasoning processes. To this end, RAG is ideal for organizations wanting to diagnose model performance issues more easily, improve transparency for AI users and customers, and better adhere to privacy regulations demanding AI explainability.
Challenges and considerations for integrating RAG in NLP
While RAG can significantly improve NLP functionality, success depends on an enterprise’s integration strategy. Organizations should consider factors like data quality and use case requirements before investing in RAG development.
Data quality
NLP performance relies heavily on the quality of data used to build a knowledge base. If this information is biased, irrelevant, incorrect, or outdated, the model will reflect this in its outputs, negating the original goal of improving accuracy.
Use case suitability
According to research on retrieval-augmented language models, RAG-enhanced NLP is not suitable for every use case. RAG is strongest in domain-intensive problems benefitting from factual information retrieval, especially in fields where current standards, practices, or insights evolve regularly. Tasks that rely on NLP’s creative strengths and core aspects of a model’s behavior, like writing style, tend to benefit less from RAG. For example, RAG can effectively support summarization tasks or question-answer systems, but it’s not as useful for achieving a specific conversational tone for a customer chatbot.
Development costs
One of the primary benefits of integrating RAG with NLP is its relatively low cost compared to fine-tuning. However, organizations will still need to consider the cost of retrieval architecture development as well as data management and storage, which can increase significantly with larger knowledge bases. Some use cases may also require a hybrid approach, combining RAG with fine-tuning, which can dramatically raise development costs.
Security
RAG introduces new security vulnerabilities to your AI systems. In particular, the technique’s retrieval phase is vulnerable to prompt-based attacks, in which attackers use cleverly worded queries to trick the model into revealing sensitive data. For example, attackers may use queries that cause a model to retrieve information similar in context or meaning to proprietary documents. This enables adversaries to get a sense of the private information stored in a knowledge base without needing to directly infiltrate the system.
Efficiency and scalability
With RAG applications, models take the additional step of retrieving knowledge base data before responding to user queries, resulting in a slightly slower system. Larger knowledge bases can delay this process further. While this may not be an issue for some applications, organizations should consider increased latency when using RAG for rapid information retrieval cases.
Ensuring success for your enterprise use case
RAG effectively addresses NLP limitations, enabling models to retrieve current, factual information without the time and resource commitments of fine-tuning. While RAG and NLP function well together, you must consider whether combining these techniques will improve your AI output reliability in practice.
For successful integration, investigate whether your use case is suitable for RAG and, if needed, combine RAG with techniques like fine-tuning to optimize NLP performance. As a first step in knowledge base development, gather only high-quality, accurate, and relevant data for your target task to ensure reliable results.
Use security best practices to protect your AI tools from common malicious attack methods like prompt injection. This includes strict user access controls for NLP systems, as well as sanitizing, encrypting, and backing up all knowledge base data. Invest in continuous monitoring and alerting solutions designed to detect anomalies unique to AI, such as suspicious prompts.
RAG is an important innovation in the field of AI, improving NLP reliability for a variety of domain-focused enterprise use cases, from analyzing financial data with current market trends to supporting customer chatbots with the latest product documentation. To maintain optimal performance, stay up-to-date with emerging techniques and adapt your RAG application based on the latest best practices.
Subscribe to
the Shift!
Get emerging insights on innovative technology straight to your inbox.
