What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs
As large language models (LLMs) grow in influence, understanding their decision-making becomes crucial. This paper introduces a method to detect concepts within LLM embeddings using low-cost linear probes, enabling monitoring of what models "think" during normal operation. The authors demonstrate concept delineation, probe training, and cross-context tracking across four concepts and three LLMs, paving the way for scalable model transparency.
Article intelligence
Key points
- Proposes linear probes to detect concepts in LLM embeddings for low-cost internal monitoring.
- Details dataset creation, probe training/testing, and tracking across larger contexts.
- Validates on four concepts and three LLMs, showing feasibility and scalability.
Why it matters
This matters because proposes linear probes to detect concepts in LLM embeddings for low-cost internal monitoring.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28823] What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs
[Submitted on 7 Apr 2026]
Title:What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs
View a PDF of the paper titled What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs, by Mohamed Abdelwahab and 7 other authors
View PDF HTML (experimental)
Abstract:As the influence of LLMs expands, it is imperative to gain insight into their decisions. One way to do that is to develop probes that detect the presence or absence of a broad set of concepts within the embeddings computed in an LLM - which is what we might say a model is "thinking" about. Such probes should be low-cost and easily applicable to any LLM, so that monitoring for many concepts is possible during normal operation.
In this paper, we take the first steps towards developing the capability of creating many such probes by defining and executing examples of the key tasks needed: first, the careful delineation of a concept through the creation of a dataset with the concept both present and then absent. Then, the training and testing of a set of linear probes to detect the concept on any layer of an LLM, including an exploration of the complexity of the probe needed. Finally, we show that such probes can track concepts across larger contexts. This is done with four separate concepts and three different LLMs. When this process is scaled to many more concepts, it will create the ability to easily monitor new models.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2605.28823 [cs.CL]
(or arXiv:2605.28823v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.28823
arXiv-issued DOI via DataCite
Submission history
From: Mohamed Abdelwahab [view email] [v1] Tue, 7 Apr 2026 03:50:09 UTC (12,636 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled What are They Thinking? Delineation, Probing and Tracking of Concepts in LLMs, by Mohamed Abdelwahab and 7 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CL
new | recent | 2026-05
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)