---
title: "Analyzing Transformers in Embedding Space — Dar 2023"
description: "Most Transformer interpretability work runs inputs through a model and inspects the activations."
authors: ["Guy Dar", "Mor Geva", "Ankit Gupta", "Jonathan Berant"]
year: 2023
venue: "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2023.acl-long.893/"
doi: "10.18653/v1/2023.acl-long.893"
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/dar-2023-analyzing-transformers-embedding-space/"
---
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# Analyzing Transformers in Embedding Space

**Authors:** Guy Dar, [Mor Geva](https://md.research.elamur.ai/free/en/authors/mor-geva/), [Ankit Gupta](https://md.research.elamur.ai/free/en/authors/ankit-gupta/), [Jonathan Berant](https://md.research.elamur.ai/free/en/authors/jonathan-berant/) · **Year:** 2023 · **Venue:** Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2023.acl-long.893/) · [PDF](https://aclanthology.org/2023.acl-long.893.pdf) · [DOI 10.18653/v1/2023.acl-long.893](https://doi.org/10.18653/v1/2023.acl-long.893) · [OpenAlex](https://openalex.org/W4385571791)

**Citations:** 49 citations (openalex-snapshot · 2026-10-14) · [80](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Most Transformer interpretability work runs inputs through a model and inspects the activations. That is costly, and the conclusions depend on the inputs chosen. Earlier static approaches covered only feed-forward value vectors or two-layer attention-only networks. Dar et al. generalize these into one input-independent framework that requires no forward or backward pass. Every trained parameter is multiplied by the embedding matrix, which casts it into vocabulary space. Attention matrices (W_QK, W_VO) then read as interactions between pairs of vocabulary items, and FF keys and values as distributions over tokens.

The experiments center on GPT-2 medium, because the framework needs a linear LM head. Projected parameters turn out to be human-interpretable. After fine-tuning on movie-review sentiment, the parameter changes surface movie-sentiment words. Because the embedding space depends only on the tokenizer, it also serves as a common reference for models sharing a vocabulary. This lets the authors align independently trained BERT models and transfer a classifier into another model without training.

## Key points
- The framework interprets FF keys, FF values, W_QK and W_VO by projecting them through the embedding matrix E. Biases and layer normalization are excluded.
- BERT instances pretrained from different random seeds align layer by layer in embedding space: layer i of one matches layer i of the other. Doing the same comparison in feature space produces no meaningful pattern.
- The zero-shot "stitching" experiment works as follows:
  - Hidden states of a GPT-2 medium (24-layer) IMDB classifier are mapped through the kernel E_A·E_B⁺, which uses a pseudo-inverse.
  - The mapped states feed the last three fine-tuned layers of a 12-layer GPT-2 base model.
  - In 3 of 11 runs, stitching at top layers reached about 70% IMDB accuracy, against a 50% baseline.
- The main limitation is that architectures with non-linear LM heads (BERT, RoBERTa, T5) are not formally covered. Even so, alignment worked in practice for BERT.
- Code is released at github.com/guyd1995/embedding-space.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

80 papers of our library cite it (of the 140884 whose bibliographies we read); 3 of them have a page here, 77 not yet.

- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
- [In-Context Learning Creates Task Vectors](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/)
- [Tug-of-war between idioms' figurative and literal interpretations in LLMs](https://md.research.elamur.ai/free/en/papers/oh-2026-tug-war-between-idioms-figurative/)

## Cite

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

```bibtex
@inproceedings{dar2023analyzing,
  title = {{Analyzing Transformers in Embedding Space}},
  author = {Dar, Guy and Geva, Mor and Gupta, Ankit and Berant, Jonathan},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-long.893},
  url = {https://doi.org/10.18653/v1/2023.acl-long.893}
}
```

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