# Inspection and Control of Self-Generated-Text Recognition Ability in Llama3-8b-Instruct

arXiv: https://arxiv.org/abs/2410.02064 · HTML: https://mlsanity.com/paper/2410.02064/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Self-rewarding alignment and self-training, one of 13 directions in Synthetic data and self-improvement for LLMs.
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Placed in

- Synthetic data and self-improvement for LLMs / [Self-rewarding alignment and self-training](https://mlsanity.com/synth/self-rewarding-alignment-and-self-training/)

## Builds on (most cited first, 5 of 5)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Representation Engineering: A Top-Down Approach to AI Transparency](https://mlsanity.com/paper/2310.01405/) (2023)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Eliciting Latent Predictions from Transformers with the Tuned Lens](https://mlsanity.com/paper/2303.08112/) (2023)

## Built on by (most cited first, 17 of 17)

- [Know Thyself? On the Incapability and Implications of AI Self-Recognition](https://mlsanity.com/paper/2510.03399/) (2025)
- [From Simulation to Enaction: Post-trained language models recognize and react to their own generations](https://mlsanity.com/paper/2605.25459/) (2026)
- [Are LLM Evaluators Really Narcissists? Sanity Checking Self-Preference Evaluations](https://mlsanity.com/paper/2601.22548/) (2026)
- [ContextEcho: A Benchmark for Persona Drift in Long Agentic-Coding Sessions](https://mlsanity.com/paper/2605.24279/) (2026)
- [Mitigating Many-Shot Jailbreaking](https://mlsanity.com/paper/2504.09604/) (2025)
- [Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators](https://mlsanity.com/paper/2509.03647/) (2025)
- [Evaluating Large Language Model Raters for German Open-Response Clinical Questions: A Physician-Annotated Benchmark Study of Agreement, Evaluator Bias, and Abstention](https://mlsanity.com/paper/2607.01103/) (2026)
- [Self-Recognition Finetuning can Prevent and Reverse Emergent Misalignment](https://mlsanity.com/paper/2606.23700/) (2026)
- [Self-Generated Text Recognition: Quality Heuristics, Cross-Task Transfer, and Downstream Bias in LLM Evaluation](https://mlsanity.com/paper/2608.26159/) (2026)
- [Robust Uncertainty Quantification for Factual Generation of Large Language Models](https://mlsanity.com/paper/2601.00348/) (2026)
- [The Assistant as a Privileged Persona: A canonical reference in cross-persona self-recognition](https://mlsanity.com/paper/2606.00545/) (2026)
- [LLM Self-Recognition: Steering and Retrieving Activation Signatures](https://mlsanity.com/paper/2606.06315/) (2026)
- [Telescope: Improving Zero Shot Detection of LLM Generated Content By Measuring Token Repetition Probability](https://mlsanity.com/paper/2607.04061/) (2026)
- [Strangers to Themselves: What Language Models Say About Themselves Is Generic](https://mlsanity.com/paper/2609.09899/) (2026)
- [Style, Not Self: Surface Cues Explain Zero-Shot Code Attribution by Large Language Models](https://mlsanity.com/paper/2609.30048/) (2026)
- [Where Is the "Self" of a Large Language Model? -- From Self-Index to Self-Reference](https://mlsanity.com/paper/2610.16717/) (2026)
- [Cross-Model In-Context Neurofeedback Reveals (Limited) Activation Control in LLMs](https://mlsanity.com/paper/2610.20179/) (2026)

## Abstract (arXiv)

It has been reported that LLMs can recognize their own writing. As this has potential implications for AI safety, yet is relatively understudied, we investigate the phenomenon, seeking to establish whether it robustly occurs at the behavioral level, how the observed behavior is achieved, and whether it can be controlled. First, we find that the Llama3-8b-Instruct chat model - but not the base Llama3-8b model - can reliably distinguish its own outputs from those of humans, and present evidence that the chat model is likely using its experience with its own outputs, acquired during post-training, to succeed at the writing recognition task. Second, we identify a vector in the residual stream of the model that is differentially activated when the model makes a correct self-written-text recognition judgment, show that the vector activates in response to information relevant to self-authorship, present evidence that the vector is related to the concept of "self" in the model, and demonstrate that the vector is causally related to the model's ability to perceive and assert self-authorship. Finally, we show that the vector can be used to control both the model's behavior and its perception, steering the model to claim or disclaim authorship by applying the vector to the model's output as it generates it, and steering the model to believe or disbelieve it wrote arbitrary texts by applying the vector to them as the model reads them.