You ask for an article draft or translation, and the results consistently show the same style, structure, and tone. You keep tweaking the prompt, switching tools, but the output hits the same note every single time.
Is there a way to break the AI repetition loop?
🔂 The LLM repetition problem 🔗
The repeat curse in large language models has become hard to ignore – for everyday users and researchers alike. According to Weichuan Wang and Zhaoyi Li, the authors of the paper “Mitigating the Language Mismatch and Repetition Issues in LLM-based Machine Translation via Model Editing”, models used for translation frequently exhibit two patterns of errors: repetition and language mismatch. These errors badly affect translation quality and may lead to generic and flattened results.
The LLM repetition problem occurs because AI models are trained to predict results based on probability. Each word is selected according to the highest likelihood of following the previous one. When a model leans too heavily on its top-probability choices, it falls into prediction loops, creating the same polite-but-generic tone, familiar metaphors, and mirrored sentence structures.
But it gets worse. When the same probability is applied for decoding, the generated text becomes even more repetitive. The model learns what comes most often, and a decoding strategy that selects the top-probability token amplifies that tendency. Your AI outputs are once again packed with common phrases and monotonous paragraphs. This “greedy decoding” means the model remains in a narrow part of the token distribution, which is also the most crowded. It never looks at the farther tail with less common words.
And that’s not the end of the repetition story. When the models are trained on their own generated content, that repetition pattern is reinforced again. That’s where a model collapse happens, leading to reduced quality and diversity in outputs.
📉 Repetition affects your localization quality 🔗
If you’re localizing content for new markets, repetition is the last thing you want to add to your repertoire. Your users are not interested in another copy-paste article with the same metaphors, familiar word order, and uniform tone.
Leave your LLM playing the same high-probability notes, and your blog posts will constantly begin with the most dreaded “In today’s fast-paced world…”. Ask it to translate the string “Explore how we can transform your experience” from your fitness app into Polish, and it will most likely default to “Odkryj, jak możemy odmienić Twoje doświadczenie”, a dull, generic, literal phrase that says nothing and converts no one.
And that lack of conversions is not just a gut feeling. There’s proof for that. The “AI vs Human Copywriter Performance Statistics” published by Amra & Elma Marketing Agency shows that human-written ads gained around 45% more impressions and 60% more clicks than AI-generated ads. According to their analysis, the best approach for the marketing content overall seems to be human+AI. That’s how you can move from repetitive, boring texts to content that engages and converts.
Dull, generic sentences in your localized content are easy to spot and don't convert customers. According to a 2025 Amra & Elma's report, human-written ads get 60% more clicks than AI-generated ones
Repetitions are also harmful to your SEO. Search engines reward unique, context-rich phrasing. But AI-generated content often uses the same structures, which ultimately drags your rankings down. Repetitive outputs have a negative impact on keyword variation, as well. Instead of natural local synonyms, models reuse the same English-influenced loanword that may be less relevant for the local market.
Below, you can see some real-world examples of highly repetitive texts found online.
🪄How to stop an LLM from repeating itself 🔗
AI repetition in localization is a common issue, and the online world is full of examples such as those above. One way to avoid it is by introducing an LLM repetition penalty that discourages models from replicating the same words. It modifies token probabilities and leads to more unique results. Another effective method is to penalize repetition in training data.
Such upgraded models may or may not see daylight, so instead of waiting impatiently with high hopes, there’s something else you can do right now. And it doesn’t involve dropping the AI altogether. All you need is your wizard hat and a bit of magic in the form of verbalized sampling. 🎩
Using verbalized sampling 🔗
What is verbalized sampling, and how can it help you?
According to the paper “Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity” by Jiayi Zhang, Simon Yu, and others, verbalized sampling is an inference-time method that improves diversity in LLM outputs. It explicitly asks the model to generate multiple candidate outputs along with natural-language justifications that explain how likely each candidate is. This simple act pushes the model to investigate options it would normally ignore. And because this process happens at inference time, it restores diversity without retraining, fine-tuning, or touching the model weights.
The experiments conducted by the research authors demonstrated that verbalized sampling significantly improves performance in creative writing dialogue simulation and synthetic data generation without compromising accuracy.
It also adds more variety to translation. The model pulls out lower probability but valid choices that standard decoding would overlook. This means that your results may contain alternative sentence structures, different tones, regionally distinct phrasing, and more creative, but still faithful paraphrases.
All in all, using verbalized sampling for localization and translation can lead to more natural-sounding content that engages and converts.
A quick example 🔗
Let’s see it in action. For the following prompt:
Provide 4 alternative translations from Polish into English of the text below.
For each version, write a short explanation of how likely that translation is (high / medium / low probability) and what linguistic factors influence that probability (syntax, tone, idioms, regionality, etc.).
Source: “Odkryj świat, w którym granica między rzeczywistością a fantazją nie istnieje”.GPT 5.1 presented the following results:
As you can see, the model went below the safest choice and generated less probable but more creative translations. My personal favorite is no. 3, and yes, no. 1 is the most literal, word-for-word translation that you’d receive from most machine translation engines or AI without tailored prompts. In other words, that’s the result you probably want to avoid.
📬 Tips to get better outputs without retraining AI 🔗
Verbalized sampling is not the only cure for the natural language generation repetition. To take the repetition spell off your model, you can call on these superpowers as well:
1. Use role prompts 🔗
When life gets boring, role-playing may come to the rescue. The same applies to AI. According to the paper by Zhang and Yu, repetition often stems from typicality bias.
The model (and its human trainers) prefer outputs that sound “familiar” or “safe.” When you assign a specific role (“You are a cultural strategist,” “You are a brand tone coach,” “You are a localization strategist”), you encourage AI to sample from a different internal distribution. This small change helps the model access less typical, more contextually diverse responses.
2. Adjust temperature 🔗
Default settings create comfort, and comfort quickly turns into monotony. The same goes for low temperatures: they keep your model cautious and predictable. To spice things up a bit, move the temperature upward and let the model look into alternative completions. Higher temperatures lead to less likely and often more unique results.
3. Add constraints to your instructions 🔗
Freedom without limits might be destructive. Your AI model needs some constraints as well to mitigate the LLM repetition problem and stop damaging your content.
Research on creativity in LLMs shows that explicit restrictions push the model to explore its internal diversity instead of defaulting to the safest phrasing. This strategy limits LLM’s ability to copy text from its training data and breaks the repetition loop.
In localization, this means that you’ll need to tell an LLM what dimensions to vary, e.g., tone, format, region, or emotion. In this way, the model can spread its responses across those new axes rather than collapsing onto one “typical” answer.
4. Provide iterative feedback 🔗
If you keep doing the same and hope for a different outcome, you’re essentially running in circles. And no one enjoys that, especially when interacting with AI. The model keeps behaving the same, your prompts stay the same, yet you still expect something new to happen. At some point, someone has to step off the treadmill.
That’s where feedback comes in. Your AI model doesn’t “know” it’s stuck in the same place. You need to enlighten it.
Iterative feedback is a powerful strategy both for agentic and generative AI. It acts as a micro version of verbalized sampling: you push the model to re-sample from its wider range.
With these powerful techniques, you can move the model out of its highly probable comfort zone and into uncharted terrain. And that’s the perfect space for AI exploration and human imagination to work together. Unique, creative, and full of unexpected twists.
