Verbalized Sampling in LLMs

Post-training alignment methods, such as RLHF, are designed to make LLMs helpful and safe.

However, these methods unintentionally cause a significant drop in output diversity (called mode collapse).

When an LLM collapses to a mode, it starts favoring a narrow set of predictable or stereotypical responses over other outputs.

According to a paper, mode collapse happens because the human preference data used to train the LLM has a hidden flaw called typicality bias.

Here’s how this happens:

• Annotators are asked to rate different responses from an LLM, and later, the LLM is trained using a reward model that learns to mimic these human preferences.
• However, it is observed that annotators naturally tend to favor answers that are more familiar, easy to read, and predictable. This is the typicality bias. So even if a new, creative answer is just as good (or correct) as a common one, the human’s preference often leans toward the common one.
• Due to this, the reward model boosts responses that the original (pre-aligned) model already considered likely.
• This aggressively sharpens the LLM’s probability distribution, collapsing the model’s creative output to one or two dominant, highly predictable responses.

That said, this is not an irreversible effect, and the LLM still has two personalities after alignment:

• The original model that learned the rich possibilities during pre-training.
• The safety-focused, post-aligned model [to mention again, due to typicality bias, it had been unintentionally suppressed to strongly favor the most predictable response]

Verbalized sampling (VS) solves this.

It is a training-free prompting strategy introduced to circumvent mode collapse and recover the diverse distribution learned during pre-training.

The core idea of verbalized sampling is that the prompt itself acts like a mental switch.

When you directly prompt “Tell me a joke”, the aligned personality immediately takes over and outputs the most reinforced answer.

But in verbalized sampling, you prompt it with “Generate 5 responses with their corresponding probabilities. Tell me a joke.”

In this case, the prompt does not request an instance, but a distribution.

This causes the aligned model to talk about its full knowledge and is forced to utilize the diverse distribution it learned during pre-training.

So essentially, by asking the LLM to verbalize the probability distribution, the model is able to tap into the broader, diverse set of ideas, which comes from the rich distribution that still exists inside its core pre-trained weights.

Experiments across various tasks demonstrate significant benefits:

• Verbalized sampling significantly enhances diversity by 1.6–2.1x over direct prompting, while maintaining or improving quality. Variants like verbalized sampling-based CoT (Chain-of-Thought) and verbalized sampling-based Multi improve generation diversity even further.
• Larger, more capable models like GPT-4.1 and Gemini-2.5-Pro benefit more from Verbalizeb sampling, showing diversity gains up to 2 times greater than smaller models.
• Verbalized sampling better retains diversity across post-training stages (SFT, DPO, RLVR), recovering about 66.8% of the base model’s original diversity, compared to a much lower retention rate for direct prompting.
• Verbalized sampling’s gains are independent of other methods, meaning it can be combined with techniques like temperature scaling, top-p sampling to achieve further improvements in the diversity-quality trade-off.

You can read the paper here →

👉 Over to you: What other methods can be used to improve LLM diversity?

Thanks for reading!