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Overcoming LLM Hallucinations Using Retrieval Augmented Generation (RAG)

Large Language Models (LLMs) are revolutionizing how we process and generate language, but they’re imperfect. Just like humans might see shapes in clouds or faces on the moon, LLMs can also ‘hallucinate,’ creating information that isn’t accurate. This phenomenon, known as LLM hallucinations, poses a growing concern as the use of LLMs expands.

Mistakes can confuse users and, in some cases, even lead to legal troubles for companies. For instance, in 2023, an Air Force veteran Jeffery Battle (known as The Aerospace Professor) filed a lawsuit against Microsoft when he found that Microsoft’s ChatGPT-powered Bing search sometimes gives factually inaccurate and damaging information on his name search. The search engine confuses him with a convicted felon Jeffery Leon Battle.

To tackle hallucinations, Retrieval-Augmented Generation (RAG) has emerged as a promising solution. It incorporates knowledge from external databases to enhance the outcome accuracy and credibility of the LLMs. Let’s take a closer look at how RAG makes LLMs more accurate and reliable. We’ll also discuss if RAG can effectively counteract the LLM hallucination issue.

Understanding LLM Hallucinations: Causes and Examples

LLMs, including renowned models like ChatGPT, ChatGLM, and Claude, are trained on extensive textual datasets but are not immune to producing factually incorrect outputs, a phenomenon called ‘hallucinations.’ Hallucinations occur because LLMs are trained to create meaningful responses based on underlying language rules, regardless of their factual accuracy.

A Tidio study found that while 72% of users believe LLMs are reliable, 75% have received incorrect information from AI at least once. Even the most promising LLM models like GPT-3.5 and GPT-4 can sometimes produce inaccurate or nonsensical content.

Here’s a brief overview of common types of LLM hallucinations:

Common AI Hallucination Types:

  1. Source Conflation: This occurs when a model merges details from various sources, leading to contradictions or even fabricated sources.
  2. Factual Errors: LLMs may generate content with inaccurate factual basis, especially given the internet’s inherent inaccuracies
  3. Nonsensical Information: LLMs predict the next word based on probability. It can result in grammatically correct but meaningless text, misleading users about the content’s authority.

Last year, two lawyers faced possible sanctions for referencing six nonexistent cases in their legal documents, misled by ChatGPT-generated information. This example highlights the importance of approaching LLM-generated content with a critical eye, underscoring the need for verification to ensure reliability. While its creative capacity benefits applications like storytelling, it poses challenges for tasks requiring strict adherence to facts, such as conducting academic research, writing medical and financial analysis reports, and providing legal advice.

Exploring the Solution for LLM Hallucinations: How Retrieval Augmented Generation (RAG) Works

In 2020, LLM researchers introduced a technique called Retrieval Augmented Generation (RAG) to mitigate LLM hallucinations by integrating an external data source. Unlike traditional LLMs that rely solely on their pre-trained knowledge, RAG-based LLM models generate factually accurate responses by dynamically retrieving relevant information from an external database before answering questions or generating text.

RAG Process Breakdown:

Steps of RAG

Steps of RAG Process: Source

Step 1: Retrieval

The system searches a specific knowledge base for information related to the user’s query. For instance, if someone asks about the last soccer World Cup winner, it looks for the most relevant soccer information.

Step 2: Augmentation

The original query is then enhanced with the information found. Using the soccer example, the query “Who won the soccer world cup?” is updated with specific details like “Argentina won the soccer world cup.”

Step 3: Generation

With the enriched query, the LLM generates a detailed and accurate response. In our case, it would craft a response based on the augmented information about Argentina winning the World Cup.

This method helps reduce inaccuracies and ensures the LLM’s responses are more reliable and grounded in accurate data.

Pros and Cons of RAG in Reducing Hallucinations

RAG has shown promise in reducing hallucinations by fixing the generation process. This mechanism allows RAG models to provide more accurate, up-to-date, and contextually relevant information.

Certainly, discussing Retrieval Augmented Generation (RAG) in a more general sense allows for a broader understanding of its advantages and limitations across various implementations.

Advantages of RAG:

  • Better Information Search: RAG quickly finds accurate information from big data sources.
  • Improved Content: It creates clear, well-matched content for what users need.
  • Flexible Use: Users can adjust RAG to fit their specific requirements, like using their proprietary data sources, boosting effectiveness.

Challenges of RAG:

  • Needs Specific Data: Accurately understanding query context to provide relevant and precise information can be difficult.
  • Scalability: Expanding the model to handle large datasets and queries while maintaining performance is difficult.
  • Continuous Update: Automatically updating the knowledge dataset with the latest information is resource-intensive.

Exploring Alternatives to RAG

Besides RAG, here are a few other promising methods enable LLM researchers to reduce hallucinations:

  • G-EVAL: Cross-verifies generated content’s accuracy with a trusted dataset, enhancing reliability.
  • SelfCheckGPT: Automatically checks and fixes its own errors to keep outputs accurate and consistent.
  • Prompt Engineering: Helps users design precise input prompts to guide models towards accurate, relevant responses.
  • Fine-tuning: Adjusts the model to task-specific datasets for improved domain-specific performance.
  • LoRA (Low-Rank Adaptation): This method modifies a small part of the model’s parameters for task-specific adaptation, enhancing efficiency.

The exploration of RAG and its alternatives highlights the dynamic and multifaceted approach to improving LLM accuracy and reliability. As we advance, continuous innovation in technologies like RAG is essential for addressing the inherent challenges of LLM hallucinations.

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