Google’s AI: LLMs Decoding Fake Idioms and Hallucinations

Introduction

Last month, the made-up saying “You can’t lick a badger twice” stormed social media as a vivid example of Google Search’s AI Overview feature confidently explaining idioms that never existed. Users discovered that by appending “meaning” to any random string of words in the Google Search bar, the AI Overview—powered by Google’s large-scale language models (LLMs)—would generate an authoritative-sounding interpretation, complete with invented etymologies and “historical” references.

In this expanded article, we explore how Google’s LLMs generate these explanations, why they sometimes hallucinate sources, and what this reveals about the current state of AI in search. We’ll draw on the latest updates from Google I/O 2025, technical specifications of Google’s PaLM 2 and Gemini models, expert commentary on LLM hallucinations, and emerging best practices to mitigate over-confident AI assertions.

Human vs. LLM: A Thought Experiment

Imagine a child asking you to explain “You can’t lick a badger twice.” A typical human response might be:

• “I’ve never heard that phrase; can you tell me where you saw it?”
• “It doesn’t make sense—there’s no cultural context or tradition behind it.”

Faced with persistence, a helpful adult might improvise a metaphor: associating “lick” with “outsmart,” drawing on known idioms like “Fool me once…” and noting the badger’s tenacity. In contrast, Google’s AI Overview bypasses dialogue and immediately offers a single, confident explanation, attributing meanings and origins without any caveats—sometimes even fabricating quotes, sources, or historical events.

Technical Architecture Behind AI Overviews

Under the hood, Google’s AI Overviews leverage a hybrid architecture combining:

• PaLM 2 & Gemini: The base LLMs trained on diverse web corpora, fine-tuned for question answering and summarization tasks.
• Knowledge Graph Integration: A structured database of real-world entities used to ground named facts, when available.
• Retrieval-Augmented Generation (RAG): In some experiments announced at Google I/O 2025, Google is testing a RAG pipeline that fetches relevant passages from its index before prompting the LLM—a move designed to reduce hallucinations.

Despite these components, current deployments often default to pure generative output when no matching Knowledge Graph node or indexed passage exists for a novel phrase. As a result, the LLM extrapolates freely, producing “explanations” that sound plausible but may lack verifiable grounding.

Case Studies: From Badgers to Tortoises

Publicly shared examples highlight both impressive leaps in semantics and worrying leaps into fiction:

• You can’t lick a badger twice: Interpreted as “once deceived, one remains wary of the same trick,” complete with a spurious tie to historical badger-baiting sports.
• You can’t humble a tortoise: Framed as a cautionary metaphor about immutable character, even though no culture records this saying.
• When you see a tortoise, spin in a circle: The lone example where the AI Overview admitted “this isn’t a standard expression”, then tentatively linked it to Japanese children’s rhymes—an encouraging sign of calibrated uncertainty.

Expert Perspectives on LLM Hallucinations

Leading AI researchers emphasize that large language models lack explicit “knowledge checks”:

• Sam Altman, OpenAI: “LLMs interpolate patterns but have no internal fact-verification layer—hallucinations are inevitable unless anchored by retrieval or symbolic reasoning.”
• Dr. Emily Bender, University of Washington: “We’ve built systems that speak convincingly but can’t differentiate plausible fiction from documented truth. Users see gloss, not foundations.”

At Google I/O 2025, Google’s VP of Search, Liz Reid, acknowledged these challenges and announced upcoming “Fact Shield” mechanisms—a combination of real-time source tagging and confidence scoring meant to signal uncertainty when the model operates beyond its data support.

Mitigation Strategies and Best Practices

To reduce the impact of over-confident AI explanations, engineers and product teams can adopt multiple strategies:

• Confidence Calibration: Expose model confidence scores or qualitative tiers (e.g., “High confidence,” “Low confidence”) in the UI.
• Source Citation: Enforce mandatory citation of indexed documents; fall back to “I’m not sure” if no source is found.
• Human-in-the-Loop Verification: Route low-confidence queries through a lightweight fact-checking layer before display.
• Prompt Engineering: Instruct the LLM to preface creative interpretations with disclaimers when asked about non-existent phrases.

Conclusion: Balancing Creativity and Accuracy

Google’s AI Overviews represent a step forward in conversational search, showcasing LLMs’ power to extract meaning—even from nonsense. Yet the same mechanisms that allow poetic interpretations also enable confident hallucinations. As the technology matures, integrating robust retrieval, calibrated uncertainty, and clear sourcing will be key to transforming AI Overviews from entertaining curiosities into truly reliable search companions.