Combating LLM Hallucinations: Practical Techniques for Enhanced Accuracy

Large Language Models

Large Language Models (LLMs) have been trained to understand intricacies of human language, which made them a powerful tool that can act as virtual assistants and be incredibly helpful. Let us look at details on 5 Best Techniques to Control Hallucination in LLMs.

However, LLMs have tendency to be creative and sometimes it gets carried away with embellishments. They can invent details, fabricate information, or simply make things up entirely. We call this phenomenon “hallucination” – when the LLM produces text that sounds amazing but simply isn’t true.

While this might sound like a harmless quirk, hallucinations can have serious consequences, especially when LLMs are used in critical tasks where factual accuracy is unavoidable!!

In this blog post, we’ll explore why they hallucinate, and uncover some of the key techniques we can use  to overcome hallucinations in LLMs.

Why Do LLMs Hallucinate?

Imagine a child learning a language. They soak up words and phrases like a sponge, mimicking what they hear and piecing together the wonderful world of communication. But just like a child might misinterpret a metaphor or get a story slightly wrong, LLMs can also stumble on the path to understanding language.

This is where hallucinations creep in.

LLMs are incredibly good at identifying patterns and making connections between words. They see that “blue skies” often appear with “sunshine” and “sandy beaches”.

So, if you ask an LLM to describe a summer day, it might confidently tell you about sparkling blue skies, even if the actual data included a story about a summer day with a sudden downpour. The LLM simply hasn’t been trained to distinguish between the typical and the exceptional.

Another culprit is bias. The vast datasets used to train LLMs can reflect the biases present in the real world. Imagine a dataset containing mostly articles written by men. The LLM, trained on this data, might generate text that reinforces gender stereotypes, even if it’s not explicitly programmed to do so. These biases can lead to hallucinations that perpetuate misinformation or unfair assumptions.

Finally, LLMs are statistical oracles. They predict the most likely next word in a sequence, based on the patterns they’ve observed. This can be fantastic for generating creative text formats like poems or code. However, the LLM might prioritize fluency over truth, and combine words that lack factual grounding.

Understanding these reasons behind LLM hallucinations is crucial as it allows us to develop techniques to mitigate them and ensure these powerful language models not just become helpful tool but reasonably trustworthy too.

5 Best Techniques to Reduce Hallucinations in LLMs

Here are some techniques to reduce hallucinations in large language models (LLMs):

1. Prompt Engineering

Imagine you’re giving directions to a friend in a new city. If you simply say “Go north,” they might lost easily in a maze of alleyways. But if you provide clear instructions, like “Turn left at the bakery and walk for two blocks until you see the park”, they’ll have a much better chance of arriving at their destination.

The same principle applies to LLMs. To steer them away from hallucinations and towards factual accuracy, we can use a technique called prompt engineering.

Here, we craft the questions we ask the LLM with care, acting as a guide on their journey through the vast ocean of information.

Imagine you’re giving directions to a friend in a new city. If you simply say “Go north,” they might lost easily in a maze of alleyways. But if you provide clear instructions, like “Turn left at the bakery and walk for two blocks until you see the park”, they’ll have a much better chance of arriving at their destination.

The same principle applies to LLMs. To steer them away from hallucinations and towards factual accuracy, we can use a technique called prompt engineering.

Here, we craft the questions we ask the Large Language Models with care, acting as a guide on their journey through the vast ocean of information.

There are several ways to do this:

• Instructing for Accuracy

Just like giving clear directions, a specific and concise prompt sets the LLM on the right track.

A common practice involves explicitly directing the LLM to avoid generating false or unverifiable information. This is often included in the “system prompt,” acting as a reminder for the LLM to prioritize accurate outputs.

System Prompt: “If you don’t know the answer to a question, please don’t share false information”.

• Few-Shot Prompting

Sometimes, showing is better than telling. When prompting the LLM for a particular type of text, provide relevant examples.

Just like showing a child how to draw a cat by providing a few simple sketches. Few-shot prompting works in a similar way. We can offer the LLM a handful of relevant examples to illustrate the desired format and content.

This helps the LLM focus on the specific topic and understand the context, leading to more accurate and consistent responses.

However, keep in mind that the quality of the examples is also important. Inaccurate or biased examples can backfire and lead to even more hallucinations.

• Chain-of-Thought Prompting

Sometimes, the most effective way to reach a destination is to understand the journey itself. Chain-of-thought prompting encourages the LLM to reveal its reasoning steps before delivering the final answer.

This can involve simply asking the Large Language Models to “Think step-by-step” or providing specific reasoning examples for it to follow. By demystifying the LLM’s thought process, we can identify and address any potential biases or factual errors lurking within its reasoning chain.

However, it’s important to note that chain-of-thought prompting itself can introduce new challenges, such as the possibility of “hallucinated reasoning” where the LLM invents steps that aren’t grounded in reality.

2. Expanding the Context Window with Data Augmentation

Imagine trying to write a story with only a single sentence as a starting point. It’s a recipe for disjointed narratives. Similarly, LLMs struggle when the context they’re given is too limited.

Data augmentation comes to the rescue in these situations. This technique involves adding relevant background information that exceeds the LLM’s typical context window. This could involve breaking down complex topics into smaller chunks or providing additional reference materials.

You should specify or describe your requirements in the prompt to guide the LLM for desired results as shown below:

• Instead of asking “write a story”,
• Try “write a science fiction story about a robot who questions its humanity”.

By giving the LLM a broader picture, we can enable it to generate more cohesive and accurate responses.

3. Focused Training – Sharpening the LLM’s Saw

Imagine you’re training for a marathon. You wouldn’t just spend all your time juggling or playing hopscotch, would you? Effective training requires focus and specialization. The same principle applies to LLMs. While they’re trained on massive datasets, sometimes they need a more targeted approach to reduce hallucinations and excel in specific tasks.

This is where fine-tuning comes in, acting like a sharpening stone for the LLM’s abilities. Here, we take a general-purpose LLM and expose it to a more focused dataset tailored to a particular use case. Think of it like training a chef to specialize in French cuisine, rather than expecting them to be a master of every culinary tradition.

For example, an LLM trained on a general web crawl might struggle with complex medical terminology. However, by fine-tuning it on a dataset of medical research papers and journals, we can significantly improve its understanding of medical concepts. This focused training allows the LLM to develop specialized knowledge and reasoning patterns relevant to the specific domain.

4. Retrieval-Augmented Generation (RAG)

Suppose a student writing a research paper but only having access to a single textbook. Their work might be well-written, but it would lack the depth and breadth that comes from consulting a variety of sources.

Retrieval-Augmented Generation (RAG) tackles LLM hallucinations in a similar way. This technique acts like a bridge, connecting the LLM’s internal world to a vast reservoir of factual information.

Here’s how RAG works:

• Combining Strengths: RAG seamlessly combines the LLM’s creative power with access to external knowledge sources. The LLM handles the creative storytelling elements, while the external source provides factual grounding. These sources can be databases, knowledge graphs, or even an organization’s private data repositories.
• Targeted Search: When the LLM encounters a prompt, RAG doesn’t just throw the entire library of information at it. Instead, it acts like a smart librarian, searching external sources for information specifically relevant to the task at hand.

For example, if you ask an LLM with RAG capabilities to write a press release about a new product launch, RAG might search the company’s product database for technical specifications and marketing materials.

5. Knowledge Retrieval

Have you ever explored a foreign city without a map?

Sure, you might love the charming alleys, but getting lost and wandering in circles is a distinct possibility.

Knowledge retrieval acts as a map for LLMs that guides them through the vast and complex ocean of information. This technique helps them avoid getting lost in the labyrinth of their own internal knowledge and ensures their responses are real.

For example, if you ask an LLM to write a biography of a historical figure, knowledge retrieval might take key details like their birthdate, birthplace, and major achievements from the knowledge graph.

This information is then transformed into a format the LLM can readily process and incorporate into its response. Equipped with this knowledge map, the LLM can generate a more accurate and informative biography, and avoid getting lost in the maze of historical details.

Final Words!

Large Language Models are revolutionizing the way we interact with information. Their ability to generate human-quality text, translate languages, and answer our questions is truly remarkable. However, as with any powerful tool, LLMs come with their own challenges – “Hallucinations”!

The good news is that we’re not powerless. By employing these techniques, we can guide LLMs towards factual accuracy.

As we continue to refine these techniques, the future of LLMs is bright. Imagine a world where LLMs become not just creative storytellers, but also trusted advisors, ever-ready to answer our questions with accuracy and insight.

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