Advanced Hallucination Mitigation Techniques in LLMs – RAG, knowledge editing, contrastive decoding, self-refinement, uncertainty-aware beam search

Author(s): Mohit Sewak, Ph.D.

Originally published on Towards AI.

Advanced Hallucination Mitigation Techniques in LLMs: Beyond RAG to a World of Riches

Stuck in the RAG rut? Break free and discover other innovative and powerful techniques like knowledge editing, contrastive decoding, self-refinement, uncertainty-aware beam search, and many more.

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Introduction: “The Problem with Hallucinations”

Picture this: You’re asking your Large Language Model (LLM) to summarize the history of tea. With confidence, it replies, “Tea was invented by a squirrel in 2500 BC to stay awake during winter hibernation.” Wait, what? Unless squirrels were ancient alchemists, that’s a bona fide hallucination — a moment where the LLM concocts something so wild it might make Hollywood writers jealous.

Hallucinations in LLMs aren’t just about quirky trivia gone rogue. In serious contexts — like healthcare, finance, or legal advice — they can lead to misinformation with real-world consequences. The irony? These hallucinations emerge from the same power that makes LLMs so good: their ability to predict coherent text by analyzing vast patterns in data.

Now, you might say, “But RAG (Retrieval-Augmented Generation) has got this under control, right?” Sure, RAG has been the knight in shining armor, retrieving external facts to ground responses. But relying solely on RAG is like patching a leaky boat with duct tape — it works, but only up to a point. It’s time we explore newer, shinier tools to tame the hallucination beast.

In this article, we’ll venture beyond RAG into the uncharted territories of advanced mitigation techniques. From Knowledge Editing (the surgeon’s scalpel) to Contrastive Decoding (the ultimate fact-checker), each method is a superhero in the making. Buckle up, grab your masala chai, and let’s dive into a world where hallucinations meet their match.

Section 1: “RAG — A Safety Net with Holes”

Retrieval-Augmented Generation, or RAG, is the dependable workhorse of hallucination mitigation. Its charm lies in its simplicity: fetch relevant information from external sources (like knowledge bases or vector databases) and hand it over to the LLM as context for text generation. Think of it as giving your LLM a cheat sheet before the big test.

But here’s the catch: RAG isn’t infallible. For starters, its effectiveness hinges on the quality of the retrieved data. If your vector database is filled with outdated or irrelevant information, the LLM might as well be guessing answers from a magic 8-ball. Worse, RAG doesn’t inherently verify its sources, leaving room for sneaky hallucinations to creep in.

Then there’s the issue of dependency. By relying solely on external retrieval, we risk turning the LLM into an overconfident intern who parrots whatever they read online — hardly the paragon of reliability we’re aiming for. Plus, the process of retrieving and integrating external data can slow things down, making RAG less practical for real-time applications.

But don’t get me wrong; RAG isn’t obsolete. It’s the dependable friend you call when you’re in a pinch. However, as the AI landscape evolves, we need to pair it with more sophisticated tools. Luckily, the next-gen techniques we’re about to explore promise just that.

Pro Tip: While RAG works wonders in knowledge-heavy domains, always double-check the quality of your retrieval sources. Garbage in, garbage out!

Section 2: “Knowledge Editing: The Surgeon’s Scalpel”

Imagine you’re editing an encyclopedia, and you find an entry claiming Shakespeare was a software developer. Instead of rewriting the whole article, you’d fix just that line. That’s what Knowledge Editing does for LLMs. It’s a precision tool that allows us to update specific facts within the model’s parameters without disrupting its broader capabilities.

How It Works

Knowledge Editing techniques like ROME (Rank-One Model Editing) and MEMIT (Model Editing via Memory) identify the specific neural connections responsible for a factual association. Then, like a neurosurgeon with a steady hand, they tweak those connections to replace incorrect information with the truth. MEMIT goes a step further by storing these edits in a memory module, ensuring the model remembers the corrections over time.

For example, if your model claims the Earth is flat (thanks, internet conspiracies), you can use ROME to pinpoint the weight matrices responsible for that knowledge and adjust them to say, “The Earth is an oblate spheroid.” Voilà, your model is now a geography nerd.

Why It’s Cool

Unlike traditional fine-tuning, which retrains the entire model and risks breaking unrelated functionalities, Knowledge Editing is surgical. It minimizes collateral damage, preserves the model’s performance on other tasks, and saves computational resources.

The Laugh Track

Let’s picture ROME and MEMIT as doctors in a sitcom:

• ROME: The straight-laced, no-nonsense surgeon who gets the job done in one clean swoop.
• MEMIT: The quirky neurologist who insists on adding a memory bank for every patient. Together, they keep your LLM in top shape.

Section 3: “Contrastive Decoding: The Ultimate Fact-Checker”

Imagine you’re at a party, and two friends start debating who invented the telephone. One says it’s Alexander Graham Bell; the other insists it was Thomas Edison. As the mediator, you compare their arguments, fact-check their claims, and declare the winner. That, in a nutshell, is what contrastive decoding does — but for LLMs.

What Is Contrastive Decoding?

Contrastive decoding pits two models against each other during text generation. One is the primary LLM (the know-it-all), and the other is a contrastive model (the skeptic). For every token the LLM generates, the contrastive model raises its eyebrow and goes, “Are you sure about that?” The final output is a blend of probabilities that favor tokens deemed factual and coherent by both models.

The math behind it boils down to tweaking probabilities based on a weighted difference between the primary model and the contrastive model. Think of it as having a grammar teacher who double-checks your work before you hit “submit.”

Why It Works

LLMs can sometimes generate tokens that seem plausible but lack factual grounding. By using a smaller, less hallucination-prone model for contrast, this method curbs the primary model’s overconfidence. It’s like having a devil’s advocate who questions everything, ensuring only reliable tokens make the cut.

Real-Life Analogy

Picture a talkative parrot that loves to ad-lib stories. Next to it, you place a parrot trainer who knows the facts. Every time the parrot squawks nonsense, the trainer nudges it back on track. The result? A parrot that sounds not just entertaining but also accurate.

The Humor Factor

Let’s imagine the models as siblings in a family dinner debate:

• The LLM: “I’m pretty sure Napoleon won the Battle of Waterloo.”
• The Contrastive Model: “Bruh, that’s not even close. Google it!”
  Together, they settle on the truth and avoid embarrassing the family.

Section 4: “Self-Refinement: The Model’s Self-Help Journey”

If you’ve ever written a first draft and then gone back to refine it, congratulations! You’ve already practiced self-refinement, one of the most exciting techniques in hallucination mitigation. Here, the model essentially becomes its own editor, reviewing and improving its initial output.

How It Works

Self-refinement operates in a loop:

1. The LLM generates a response.
2. It evaluates its own output for inconsistencies or hallucinations.
3. Based on that evaluation, it revises and improves the response.

This process mirrors how humans refine their thoughts before speaking. For example, if the model initially says, “The moon is made of cheese,” self-refinement kicks in to correct it to, “The moon consists of rock and dust.”

Why It’s Brilliant

Self-refinement empowers the model to use its internal knowledge more effectively. By iterating on its output, it learns to identify flaws and generate more accurate text. It’s like giving the model a journal where it can write, reflect, and grow wiser.

The Self-Help Analogy

Imagine an LLM attending a motivational workshop:

• Speaker: “Every hallucination is a stepping stone to the truth!”
• LLM: furiously taking notes
  By the end of the session, the model has not only improved its answers but also gained a newfound sense of purpose.

Fun Fact: Self-refinement aligns with the concept of reinforcement learning from human feedback (RLHF). It’s like having a personal trainer who claps every time you do a perfect push-up.

Section 5: “Uncertainty-Aware Beam Search: Playing It Safe”

If LLMs were explorers, uncertainty-aware beam search would be their map and compass. This method helps models steer clear of risky terrain — aka hallucinations — by favoring safer, more reliable paths during text generation.

The Basics

Beam search is a decoding strategy where multiple sequences (or “beams”) are explored simultaneously. In uncertainty-aware beam search, each beam is assigned a confidence score. Beams with high uncertainty — likely to lead to hallucinations — are pruned, leaving only the trustworthy ones.

Why It’s Safe

This method acts as the cautious driver who double-checks directions before making a turn. By avoiding paths with high uncertainty, it reduces the chances of generating wild, unsupported claims. Sure, it might sacrifice some creativity, but when accuracy is the goal, caution wins the day.

Analogy Time

Think of beam search as a group of hikers exploring a forest. Uncertainty-aware beam search is the guide who says, “That path looks sketchy; let’s not go there.” Thanks to this guide, the group avoids getting lost — or in the case of LLMs, generating bizarre answers.

Pro Tip: Use uncertainty-aware beam search when deploying LLMs in critical domains like healthcare or law. It’s better to be safe than sorry!

Section 6: “Iterative Querying and Reasoning: Detective Work”

If Sherlock Holmes were an LLM, he’d use iterative querying and reasoning to solve every mystery. This technique enables models to interrogate themselves repeatedly, poking holes in their own logic until only the truth remains. It’s the ultimate self-skepticism toolkit for LLMs, ensuring they’re not just saying something plausible but also correct.

How It Works

1. The LLM generates an initial response.
2. It asks itself follow-up questions or attempts alternative explanations to test the validity of its own output.
3. Based on this internal cross-examination, it refines the response to make it more accurate and consistent.

This method mirrors how detectives build their cases. They gather clues, test theories, and refine conclusions until the truth comes to light. For example, if the model generates, “Unicorns are real,” iterative querying might prompt it to ask, “What’s the scientific evidence for unicorns?” — forcing it to confront the fallacy.

Why It’s Clever

Iterative querying and reasoning exploit the LLM’s internal knowledge and reasoning capabilities. By engaging in self-dialogue, the model becomes more critical of its own outputs, reducing the chances of hallucination. It’s like having an inner Watson double-checking Sherlock’s deductions.

Detective Humor

Imagine the LLM as a trench-coat-wearing sleuth:

• LLM: “Elementary, my dear user. The capital of Brazil is Buenos Aires.”
• Watson (aka Iterative Querying): “Hold on, old chap. Isn’t it Brasília?”
  Cue a dramatic pause, followed by a corrected response. Mystery solved!

Real-World Applications

This technique shines in scenarios requiring logical reasoning or evidence-based answers. From academic research to medical diagnosis, iterative querying ensures outputs are less prone to fanciful detours.

Section 7: “Decoding Strategy Optimization: The Compass for Truth”

Decoding strategies are the unsung heroes of text generation. They determine how an LLM picks its words, and when optimized, they can steer the model away from hallucinations. Think of them as the compass guiding the LLM through the vast terrain of possible outputs.

What Are Decoding Strategies?

Decoding is the process of selecting the next token in a sequence. Strategies like greedy decoding, beam search, and nucleus sampling dictate how this selection happens. Optimized decoding strategies tweak these processes to balance fluency, diversity, and factual accuracy.

Key Techniques

1. Contrastive Decoding
   We’ve already covered this one — a tug-of-war between two models that ensures factuality.
2. Factual Nucleus Sampling
   Standard nucleus sampling selects tokens from the top percentile of probabilities. Factual nucleus sampling adds a twist: it prioritizes tokens backed by factual evidence. It’s like curating a guest list where only the well-informed are invited.
3. Monte Carlo Dropout
   This technique generates multiple outputs by applying dropout at inference time, then selects the most reliable one. Picture a robot running simulations and picking the most sensible outcome.

Why It Works

Decoding strategies operate directly at the generation level, so they don’t require retraining or external resources. They’re lightweight, flexible, and powerful tools for improving output quality.

Humor Break

Imagine an LLM navigating a treacherous mountain trail of possible outputs:

• LLM: “I’ll take this shortcut — wait, no, it leads to ‘The moon is cheese!’ Let’s try the main path.”
  Thanks to decoding optimization, it always finds the safest route.

Section 8: “Combining Forces: The Avengers of Hallucination Mitigation”

What happens when you bring together self-refinement, contrastive decoding, iterative querying, and beam search? You get an elite squad of techniques ready to obliterate hallucinations. Think of it as assembling the Avengers for the AI world, where each technique brings unique strengths to the table.

How It Works

Combining techniques means leveraging their complementary strengths. For example:

• Contrastive Decoding + RAG: Retrieve external knowledge to fill gaps, then use contrastive decoding to verify and refine the response.
• Self-Refinement + Uncertainty-Aware Beam Search: Let the model refine its outputs while steering clear of uncertain paths.
• Iterative Querying + Knowledge Editing: Combine detective-like self-interrogation with precise fact updates for ironclad reliability.

The Movie Pitch

Picture this:

• RAG: The resourceful genius with a database of knowledge.
• Contrastive Decoding: The sharp-tongued fact-checker.
• Self-Refinement: The introspective philosopher.
• Iterative Querying: The inquisitive detective.
  Together, they’re a blockbuster team saving the world from misinformation.

Why It’s the Future

No single method can eliminate hallucinations entirely. By combining forces, we can cover each technique’s blind spots, creating a system that’s robust, reliable, and ready for real-world deployment.

Conclusion: “Towards a World of Riches”

As we wrap up this wild ride through the realm of hallucination mitigation, one thing is clear: tackling hallucinations in LLMs isn’t a one-size-fits-all endeavor. It’s more like assembling a toolbox, where each method — from RAG’s trusty retrieval skills to the philosophical self-refinement — plays a vital role in ensuring our models are not just eloquent but also accurate.

Mitigating hallucinations is about balance. While some techniques, like Knowledge Editing, offer precision, others, like Iterative Querying, provide introspection. Together, they form a symphony of strategies that make LLMs safer, smarter, and more reliable.

But let’s not forget the human element in this journey. Whether it’s the developers refining these methods, researchers exploring uncharted territories, or users like us questioning the answers we receive, the fight against hallucination is a collaborative effort. After all, the goal isn’t just to make models less wrong — it’s to make them tools we can trust.

So, next time you sip your cardamom tea and marvel at the wonders of generative AI, remember the heroes working behind the scenes — those clever algorithms ensuring your LLM doesn’t claim that squirrels invented tea. Here’s to a future where hallucinations are less of a nuisance and more of a curiosity we can laugh about, all while unlocking the true potential of AI.

References & Further Reading

Knowledge Editing

• Meng, K., Bau, D., Andonian, A., & Belinkov, Y. (2022). Locating and editing factual associations in GPT. Advances in Neural Information Processing Systems, 35, 17359–17372.
• Meng, K., Sharma, A. S., Andonian, A., Belinkov, Y., & Bau, D. (2022). Mass-editing memory in a transformer. arXiv preprint arXiv:2210.07229.

Contrastive Decoding

• Li, X. L., Holtzman, A., Fried, D., Liang, P., Eisner, J., Hashimoto, T., … & Lewis, M. (2022). Contrastive decoding: Open-ended text generation as optimization. arXiv preprint arXiv:2210.15097.

Self-Refinement

• Niu, M., Li, H., Shi, J., Haddadi, H., & Mo, F. (2024). Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrieval. arXiv preprint arXiv:2405.06545.

Uncertainty-Aware Beam Search

• Zeng, H., Zhi, Z., Liu, J., & Wei, B. (2021). Improving paragraph-level question generation with extended answer network and uncertainty-aware beam search. Information Sciences, 571, 50–64.

Iterative Querying and Reasoning

• Li, W., Wu, W., Chen, M., Liu, J., Xiao, X., & Wu, H. (2022). Faithfulness in natural language generation: A systematic survey of analysis, evaluation and optimization methods. arXiv preprint arXiv:2203.05227.
• Qi, P., Lin, X., Mehr, L., Wang, Z., & Manning, C. D. (2019). Answering complex open-domain questions through iterative query generation. arXiv preprint arXiv:1910.07000.

General Techniques

• Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., … & Liu, T. (2023). A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. ACM Transactions on Information Systems.
• Perković, G., Drobnjak, A., & Botički, I. (2024, May). Hallucinations in llms: Understanding and addressing challenges. In 2024 47th MIPRO ICT and Electronics Convention (MIPRO) (pp. 2084–2088). IEEE.
• Mishra, A., Asai, A., Balachandran, V., Wang, Y., Neubig, G., Tsvetkov, Y., & Hajishirzi, H. (2024). Fine-grained hallucination detection and editing for language models. arXiv preprint arXiv:2401.06855.

Disclaimers and Disclosures

This article combines the theoretical insights of leading researchers with practical examples, and offers my opinionated exploration of AI’s ethical dilemmas, and may not represent the views or claims of my present or past organizations and their products or my other associations.

Use of AI Assistance: In preparation for this article, AI assistance has been used for generating/ refining the images, and for styling/ linguistic enhancements of parts of content.

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