---
title: "Inspecting and Editing Knowledge Representations in Language"
description: "The authors introduce REMEDI (REpresentation MEDIation), which learns an affine map that turns a… 53 references, cited by 99 papers here."
authors: ["Evan Hernandez", "Belinda Z. Li", "Jacob Andreas"]
year: 2023
venue: "arXiv:2304.00740"
source: "https://arxiv.org/abs/2304.00740"
doi: "10.48550/arxiv.2304.00740"
arxiv: "2304.00740"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hernandez-2023-inspecting-editing-knowledge-representations-language/"
---
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# Inspecting and Editing Knowledge Representations in Language Models

**Authors:** [Evan Hernandez](https://md.research.elamur.ai/free/en/authors/evan-hernandez/), [Belinda Z. Li](https://md.research.elamur.ai/free/en/authors/belinda-z-li/) and [Jacob Andreas](https://md.research.elamur.ai/free/en/authors/jacob-andreas/) · **Year:** 2023 · **Venue:** arXiv:2304.00740 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2304.00740) · [PDF](https://arxiv.org/pdf/2304.00740) · [DOI 10.48550/arxiv.2304.00740](https://doi.org/10.48550/arxiv.2304.00740) · [arXiv 2304.00740](https://arxiv.org/abs/2304.00740) · [OpenAlex](https://openalex.org/W4362598605)

**Citations:** 16 citations (OpenAlex, 2026-10-14) · [99](https://md.research.elamur.ai/free/en/papers/hernandez-2023-inspecting-editing-knowledge-representations-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Language models store facts about entities, both from pretraining and from the prompt, but these internal facts can be wrong or can override what the context says. The authors introduce REMEDI (REpresentation MEDIation), which learns an affine map that turns a natural-language attribute description into a vector. Adding that vector to the hidden state of a single entity token at one layer of a GPT-style autoregressive transformer changes what the model later generates about that entity. Training combines three terms: raising the likelihood of a target completion, suppressing the prior completion, and a KL penalty that keeps predictions for intermediate tokens stable.

The same learned encodings also act as probes. Comparing them with unedited entity representations shows whether the model already holds a given fact, which helps forecast factual or contextual errors on CounterFact. An editor trained only on context-free sentences still transfers to in-context knowledge. This suggests that background and contextual facts share one encoding, and that steering a model through its internal representations is a practical alternative to prompting.

## Key points
- **Edit rule:** the entity representation is updated as h_entity + W·h_attr + b, where h_attr is the averaged LM encoding of the attribute text. The edit is applied to one token at one layer.
- **Uses shown:**
  - correcting failures to integrate context (e.g., "Anita" being called a nurse rather than a lawyer);
  - overriding strong priors (e.g., a London Bridge located in Arizona);
  - editing without any textual prompt.
- **Error prediction on CounterFact:**
  - With prior knowledge, REMEDI beat the identity-encoding and fact-probe baselines but not a shortcut model trained directly for the task.
  - With in-context information, it beat all baselines, including the shortcut model.
- **Reference points:** a supervised error skyline reached about .94 F1 / .93 ϕ, so REMEDI's detection is useful but far from perfect. A random control scored ϕ near zero.
- **Availability:** code and data are at github.com/evandez/REMEDI. Experiments used HuggingFace Transformers and PyTorch on A100 and V100 GPUs.

*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

Also on the byline: [Artificial Intelligence in Medicine](https://ror.org/05p590m36).

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## Cite

Cite as: Evan Hernandez, Belinda Z. Li and Jacob Andreas. 2023. Inspecting and Editing Knowledge Representations in Language Models. arXiv:2304.00740.

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

```bibtex
@misc{hernandez2023inspecting,
  title = {{Inspecting and Editing Knowledge Representations in Language Models}},
  author = {Evan Hernandez, Belinda Z. Li and Jacob Andreas},
  year = {2023},
  doi = {10.48550/arxiv.2304.00740},
  eprint = {2304.00740},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2304.00740}
}
```

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*Updated 2 November 2026*