# Can Chat Templates Silence LLM Self-References?

> Published 2026-09-28 · https://www.promptzone.com/finn_pham/can-chat-templates-silence-llm-self-references-529i

A paper titled ["As a Language Model": Chat Template Switches LLM Self-Referential Voice](https://arxiv.org/abs/2609.25021) shows that the chat template alone can toggle an LLM's tendency to insert self-referential disclaimers. The finding surfaced in an active [Hacker News thread](https://arxiv.org/abs/2609.25021) that reached 101 points and 101 comments.

## What It Is / How It Works
The study isolates the chat template as the variable. Different template formats change how often models preface answers with phrases such as "As a language model..." or "I am an AI...". The template supplies the structural cues that either reinforce or suppress this behavior before any user message arrives.

## Benchmarks / Specs / Numbers
The HN discussion logged 101 points from 101 comments. Early participants noted measurable drops in disclaimer frequency when templates omitted explicit assistant-role framing. No parameter counts or latency figures appear in the thread, but the effect holds across multiple model families tested by commenters.

## How to Try It
Replace the system portion of your chat template with a minimal version that contains only turn markers and no role descriptors. Run the same prompt set with both the original and modified templates. Count disclaimer occurrences across 50–100 generations to quantify the shift.

## Pros and Cons
- Pros: Reduces repetitive disclaimers without extra system-prompt tokens; works at inference time with no retraining.
- Cons: May weaken safety refusals that rely on the same self-referential phrasing; results vary by base model and fine-tune.

## Alternatives and Comparisons
System-prompt overrides and post-processing filters are the main alternatives. System prompts add 20–40 tokens and still leak disclaimers in 15–30 % of responses according to HN reports. Post-processing requires an extra model call. Template-level changes avoid both costs.

| Method              | Token Overhead | Disclaimer Reduction | Extra Inference |
|---------------------|----------------|----------------------|-----------------|
| Chat template edit  | 0              | High                 | No              |
| System prompt       | 20–40          | Medium               | No              |
| Post-processing     | 0              | High                 | Yes             |

## Who Should Use This
Prompt engineers building customer-facing chatbots benefit most. Researchers studying alignment should test the effect before attributing disclaimers solely to training data. Teams that need explicit safety language should keep the original template.

## Bottom Line / Verdict
Chat template choice now ranks as a first-order control for LLM voice, cheaper than prompt engineering or fine-tuning.

Template edits give practitioners a direct lever over self-referential output with zero added compute.