@inproceedings{abdelwahab-etal-2026-thinking,
title = "What are They Thinking? Delineation, Probing, and Tracking of Concepts in {LLM}s",
author = "Abdelwahab, Mohamed and
Collins, Michelle Yu and
Chen, Sihan and
Zhao, Yi Cheng and
Mahmood, Zafarullah and
Zhu, Jiading and
Ali, Soliman and
Rose, Jonathan",
editor = "Chang, Kai-Wei and
Mehrabi, Ninareh and
Krishna, Satyapriya and
Das, Anubrata and
Dhamala, Jwala and
Cao, Yang Trista and
Kumarage, Tharindu and
Ramakrishna, Anil and
Christodoulopoulos, Christos and
Wan, Yixin and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul",
booktitle = "Proceedings of the 6th Workshop on Trustworthy {NLP} ({T}rust{NLP} 2026)",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.trustnlp-main.9/",
pages = "121--179",
ISBN = "979-8-89176-418-7",
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 high-level abstract 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 high-level abstract 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 monitor new models."
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<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 high-level abstract 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 high-level abstract 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 monitor new models.</abstract>
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%0 Conference Proceedings
%T What are They Thinking? Delineation, Probing, and Tracking of Concepts in LLMs
%A Abdelwahab, Mohamed
%A Collins, Michelle Yu
%A Chen, Sihan
%A Zhao, Yi Cheng
%A Mahmood, Zafarullah
%A Zhu, Jiading
%A Ali, Soliman
%A Rose, Jonathan
%Y Chang, Kai-Wei
%Y Mehrabi, Ninareh
%Y Krishna, Satyapriya
%Y Das, Anubrata
%Y Dhamala, Jwala
%Y Cao, Yang Trista
%Y Kumarage, Tharindu
%Y Ramakrishna, Anil
%Y Christodoulopoulos, Christos
%Y Wan, Yixin
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%S Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-418-7
%F abdelwahab-etal-2026-thinking
%X 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 high-level abstract 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 high-level abstract 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 monitor new models.
%U https://aclanthology.org/2026.trustnlp-main.9/
%P 121-179
Markdown (Informal)
[What are They Thinking? Delineation, Probing, and Tracking of Concepts in LLMs](https://aclanthology.org/2026.trustnlp-main.9/) (Abdelwahab et al., TrustNLP 2026)
ACL
- Mohamed Abdelwahab, Michelle Yu Collins, Sihan Chen, Yi Cheng Zhao, Zafarullah Mahmood, Jiading Zhu, Soliman Ali, and Jonathan Rose. 2026. What are They Thinking? Delineation, Probing, and Tracking of Concepts in LLMs. In Proceedings of the 6th Workshop on Trustworthy NLP (TrustNLP 2026), pages 121–179, San Diego, California. Association for Computational Linguistics.