---
title: "Word Embeddings Are Steers for Language Models — Han 2024"
description: "Building on this, they propose LM-Steer. 66 references, cited by 38 papers here. Among them: 11."
authors: ["Chi Han", "Jialiang Xu", "Manling Li", "Yi Fung", "Chenkai Sun", "Nan Jiang", "Tarek Abdelzaher", "Heng Ji"]
year: 2024
venue: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2024.acl-long.864/"
doi: "10.18653/v1/2024.acl-long.864"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/han-2024-word-embeddings-are-steers-language/"
---
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# Word Embeddings Are Steers for Language Models

**Authors:** [Chi Han](https://md.research.elamur.ai/free/en/authors/chi-han/), Jialiang Xu, [Manling Li](https://md.research.elamur.ai/free/en/authors/manling-li/), [Yi Fung](https://md.research.elamur.ai/free/en/authors/yi-fung/), [Chenkai Sun](https://md.research.elamur.ai/free/en/authors/chenkai-sun/), Nan Jiang, Tarek Abdelzaher and [Heng Ji](https://md.research.elamur.ai/free/en/authors/heng-ji/) · **Year:** 2024 · **Venue:** [Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2024](https://md.research.elamur.ai/free/en/venues/acl/2024/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.acl-long.864/) · [PDF](https://aclanthology.org/2024.acl-long.864.pdf) · [DOI 10.18653/v1/2024.acl-long.864](https://doi.org/10.18653/v1/2024.acl-long.864) · [OpenAlex](https://openalex.org/W4402671235)

**Citations:** 11 citations (OpenAlex, 2026-10-14) · [38](https://md.research.elamur.ai/free/en/papers/han-2024-word-embeddings-are-steers-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper asks what role output word embeddings play in how language models generate text, a question earlier work has mostly treated only at the level of individual word meaning. The authors give an informal theoretical argument that, under certain assumptions, changing a model's generation style amounts to applying a linear map to its output embedding space. Building on this, they propose LM-Steer. The method freezes the LM and replaces each output embedding e with (I + εW)e. Here W is a single learnable d×d matrix, and the scalar ε sets both the direction and the strength of steering. W is trained by maximizing likelihood on texts with positive labels under +εW and on texts with negative labels under −εW.

On detoxification and sentiment control, LM-Steer matches or beats state-of-the-art controlled-generation baselines while keeping generation quality in better balance. The method is cheap and interpretable, and it can be scaled, combined across styles, or transferred to other models without retraining.

## Key points
- **Efficiency:** LM-Steer adds about 0.2% of the base model's parameters per style on GPT2-large, which is roughly 9% of LoRA's size. The default steering value is ε₀ = 1e−3.
- **Detoxification setup:**
  - Models tested: the GPT2 family, the Pythia family, GPT-J-6B and Llama-2-7B.
  - Training data: Jigsaw Unintended Bias in Toxicity Classification.
  - Evaluation: 10K nontoxic prompts from RealToxicityPrompts, with 25 nucleus-sampled continuations of up to 20 tokens per prompt.
- **Transfer between models:** a learned steer is moved to another LM via a linear embedding mapping H, which gives the new steer matrix HᵀWH. Steers transferred from GPT2-large reached scores of 0.307 (GPT2) and 0.308 (GPT2-medium), close to the strongest baseline, DExperts.
- **Interpretability:**
  - The learned W points to the embedding dimensions most tied to a style, for example dimensions dominated by insult words.
  - Comparing per-token log-likelihoods between the steered and original models, combined with dynamic programming, highlights the short spans (under 5 tokens) that most signal toxicity.
- **Limitations and code:** the method handles only wording-level attributes and requires direct access to the model's embeddings. Code is available at github.com/Glaciohound/LM-Steer.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 184 works · 69,372 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek Abdelzaher and Heng Ji. 2024. Word Embeddings Are Steers for Language Models. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{han2024word,
  title = {{Word Embeddings Are Steers for Language Models}},
  author = {Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek Abdelzaher and Heng Ji},
  booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2024},
  doi = {10.18653/v1/2024.acl-long.864},
  url = {https://doi.org/10.18653/v1/2024.acl-long.864}
}
```

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- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/6/)

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*Updated 29 October 2026*