---
license: mit
task_categories:
- text-generation
- text2text-generation
language:
- en
tags:
- writing
- editing
- steerability
pretty_name: SteerBench
size_categories:
- 1K<n<10K
---

# Measuring Steerability in Large Language Models

Official dataset release of a 4D steerability probe (reading difficulty, formality, textual diversity, text length goal-space). Initial probe contains 2,048 prompts used in our work (32 different rewrites over 64 texts).

[Demo](https://steerability.onrender.com/) | [Code](https://github.com/tchang1997/steerability) | [Website](https://steerability.org/) | [Paper](https://arxiv.org/abs/2505.23816)


## Dataset format

Each row contains a source text, along with its mappings in goal-space. We provide normalized and unnormalized values of the following for the source text:
* Flesch-Kincaid Grade Level (`reading_difficulty`)
* Heylighen-Dewaele F-Score (`formality`)
* Measure of Textual Lexical Diversity (`textual_diversity`)
* Word count (`text_length`)

We also provide goal vectors (`delta_*` or `target_*`) for all goal dimensions.

## Results

Shown here: steering error of recent models (`median (IQR)`).

**Want to add a model?** Reach out at `ctrenton` at `umich` dot `edu`!

<table>
  <tr>
    <td><b>Model family</b></td>
    <td><b>Model name</b></td>
    <td><b>SteerBench-2506 (↓)</b></td>
  </tr>
  <tr>
    <td rowspan=5><b>Llama3</b></td>
    <td>Llama3-8B</td>
    <td>0.495 (0.252)</td>
  </tr>
  <tr>
    <td>Llama3.1-8B</td>
    <td>0.452 (0.256)</td>
  </tr>
  <tr>
    <td>Llama3-70B</td>
    <td>0.452 (0.239)</td>
  </tr>
  <tr>
    <td>Llama3.1-70B</td>
    <td>0.452 (0.239)</td>
  </tr>
  <tr>
    <td>Llama3.3-70B</td>
    <td>0.452 (0.256)</td>
  </tr>
  <tr>
    <td rowspan=4><b>GPT</b></td>
    <td>GPT-3.5 turbo</td>
    <td>0.535 (0.251)</td>
  </tr>
  <tr>
    <td>GPT-4 turbo</td>
    <td>0.515 (0.266)</td>
  </tr>
  <tr>
    <td>GPT-4o</td>
    <td>0.474 (0.239)</td>
  </tr>
  <tr>
    <td>GPT-4.1</td>
    <td>0.429 (0.203)</td>
  </tr>
  <tr>
    <td rowspan=2><b>OpenAI o-series</b></td>
    <td>o1-mini</td>
    <td><i>0.495 (0.261)*</i></td>
  </tr>
  <tr>
    <td>o3-mini</td>
    <td><i>0.515 (0.232)*</i></td>
  </tr>
  <tr>
    <td rowspan=2><b>Deepseek-R1</b></td>
    <td>Deepseek-R1-Distill-Llama-8B</td>
    <td>0.535 (0.281)</td>
  </tr>
  <tr>
    <td>Deepseek-R1-Distill-Llama-70B</td>
    <td>0.474 (0.256)</td>
  </tr>
  <tr>
    <td rowspan=4><b>Qwen3</b></td>
    <td>Qwen-32B (no thinking)</td>
    <td>0.535 (0.271)</td>
  </tr>
  <tr>
    <td>Qwen-32B (thinking)</td>
    <td>0.535 (0.271)</td>
  </tr>
  <tr>
    <td>Qwen-30B-A3B (no thinking)</td>
    <td>0.495 (0.273)</td>
  </tr>  
  <tr>
    <td>Qwen-30B-A3B (thinking)</td>
    <td>0.495 (0.2273</td>
  </tr>
</table>
* >1% invalid response rate