# Persuasion Tokens for Editing Factual Knowledge in LLMs

arXiv: https://arxiv.org/abs/2601.16781 · HTML: https://mlsanity.com/paper/2601.16781/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 25 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 19 of 19)

- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Language Models as Knowledge Bases?](https://mlsanity.com/paper/1909.01066/) (2019)
- [Mass-Editing Memory in a Transformer](https://mlsanity.com/paper/2210.07229/) (2022)
- [Memory-Based Model Editing at Scale](https://mlsanity.com/paper/2206.06520/) (2022)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Can We Edit Factual Knowledge by In-Context Learning?](https://mlsanity.com/paper/2305.12740/) (2023)
- [Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors](https://mlsanity.com/paper/2211.11031/) (2022)
- [Evaluating the Ripple Effects of Knowledge Editing in Language Models](https://mlsanity.com/paper/2307.12976/) (2023)
- [A Survey on Knowledge Editing of Neural Networks](https://mlsanity.com/paper/2310.19704/) (2023)
- [Defending Against Prompt Injection With a Few DefensiveTokens](https://mlsanity.com/paper/2507.07974/) (2025)
- [Give Me the Facts! A Survey on Factual Knowledge Probing in Pre-trained Language Models](https://mlsanity.com/paper/2310.16570/) (2023)
- [Position: Editing Large Language Models Poses Serious Safety Risks](https://mlsanity.com/paper/2502.02958/) (2025)
- [Has this Fact been Edited? Detecting Knowledge Edits in Language Models](https://mlsanity.com/paper/2405.02765/) (2024)
- [The Queen of England is not England's Queen: On the Lack of Factual Coherency in PLMs](https://mlsanity.com/paper/2402.01453/) (2024)
- [Identifying Knowledge Editing Types in Large Language Models](https://mlsanity.com/paper/2409.19663/) (2024)
- [BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing](https://mlsanity.com/paper/2505.01343/) (2025)
- [Tracing and Reversing Edits in LLMs](https://mlsanity.com/paper/2505.20819/) (2025)
- [Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation](https://mlsanity.com/paper/2511.05852/) (2025)
- [Enhancing Fact Retrieval in PLMs through Truthfulness](https://mlsanity.com/paper/2410.13562/) (2024)

## Built on by (most cited first, 1 of 1)

- [One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them](https://mlsanity.com/paper/2605.28839/) (2026)

## Abstract (arXiv)

In-context knowledge editing (IKE) is a promising technique for updating Large Language Models (LLMs) with new information. However, IKE relies on lengthy, fact-specific demonstrations which are costly to create and consume significant context window space. In this paper, we introduce persuasion tokens (P-Tokens) -- special tokens trained to replicate the effect of IKE demonstrations, enabling efficient knowledge editing without requiring fact-specific demonstrations. We evaluate P-Tokens across two editing datasets and three LLMs, demonstrating performance comparable to, and often exceeding, IKE. We further find that editing performance is robust to distractors with small negative effects to neighboring facts, and that increasing the number of P-Tokens improves performance. Our work addresses key limitations of IKE and provides a more practical and scalable alternative for editing LLMs.