Probing language models in llm How-ever, the complex mechanisms that link neuron’s functional co-activation with the emergent model capabilities remains largely unknown, hindering a deeper understanding and safer development of LLMs. The LTP analyzes the knowledge acquired by the LLM model and evaluates the validity of statements within a given knowledge base. Probing and transparent AI Playing with "LLM monkeys" is fascinating, but we aim to delve into the inner workings of their brains and mental processes. However, despite the widespread use of large May 9, 2024 · We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynamic driving scenarios. Therefore, Dec 2, 2024 · Introduction Probing tasks are essential tools for understanding the inner workings of Large Language Models (LLMs). Entity normalization is crucial in ensuring that information in knowledge graphs is well connected and therefore eficiently reusable. By systematically examining how different types of information are represented across layers, we gain a better understanding of how these models process language, which can inform debugging, model improvement, and Feb 17, 2025 · This critical review provides an in-depth analysis of Large Language Models (LLMs), encompassing their foundational principles, diverse applications, and advanced training methodologies. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and Mar 25, 2024 · Researchers find large language models use a simple mechanism to retrieve stored knowledge when they respond to a user prompt. We propose Sentinel, a lightweight sentence-level compression framework that Jan 13, 2025 · This paper was accepted at the Workshop Towards Knowledgeable Language Models at ACL 2024. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data. ABSTRACT Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. In this work, we present ProP (Prompting as Probing), which utilizes GPT-3, a large Language Model Sep 22, 2022 · We study this hypothesis two-fold, (1) by analyzing the LLM's causal question answering capabilities and (2) by probing the LLM's embeddings for correlations on the causal facts. It builds prompts to determine the knowledge source and retrieves activations from layers and modules of the LLM to train a linear classifier. The basic idea is simple—a classifier is trained to predict some linguistic property from a model’s representations—and has been used to examine a wide variety of models and properties. Sep 22, 2024 · Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. 2) Most prior methods target masked language models (MLMs) and don’t provide a universal solution for assessing the knowledge in generative LLMs. Going beyond their textual nature, this project proposal aims to investigate the interaction between LLMs and non-verbal communication, specifically focusing on gestures. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. We employ the Greedy Coordinate Gradient optimizer to craft prompts that compel LLMs to generate coherent responses from Probing classifiers are closely related to other concepts and technologies in the field of machine learning and natural language processing, such as: Language Models: Language models, such as BERT and GPT, often serve as the basis for probing classifiers, providing rich contextual representations for downstream analysis. Sep 21, 2023 · The reviewers appreciate the paper's timely focus on privacy leakage in large language models, a topic of growing importance. 2 days ago · In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures, which are widely recognized as meaningful indicators of human reading patterns. However, these representations are difficult to interpret, complicating our understanding of the models' learning capabilities. First, we identify a training set of facts known by LLMs through various probing strategies and then adapt embedding models to predict the LLM outputs with a linear decoder layer. In this work, we introduce graph Concept Depth is used to analyze the comprehension ability of Large Language Models (LLM) and the difficulty of a concept's understanding. In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers Feb 9, 2025 · Large language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. Jan 2, 2024 · Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. Oct 8, 2024 · By prompting the LLM in a way that contradicts its PK, we probe the model’s knowledge-sourcing behaviors. CL] 4 Feb 2025 Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concepts at Different Layers? Oct 8, 2023 · Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. In this pa-per, we propose a novel probing framework to explore the mechanisms governing the Abstract Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination prob-lems due to privacy issues and leakage of bench-mark datasets in the pre-training phase. Apr 13, 2024 · Probing Large Language Models from A Human Behavioral Perspective Abstract Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. However, LLM security is a moving target: models produce unpredictable output, are constantly updated, and the potential adver-sary is highly diverse: anyone with access to the internet and a decent 2 days ago · MONITOR is designed to compute the distance between the probability distributions of a valid output and its counterparts produced by the same LLM probing the same fact using different styles of prompts and contexts. However, the understanding of their prediction processes and internal mechanisms, such as feed-forward networks (FFN) and multi-head self-attention (MHSA), remains largely unexplored. Recent works on prompting language models claim to elicit intermediate reasoning steps and key tokens that serve as proxy explanations for LLM predictions. Language Models (LMs) have proven to be useful in various downstream applications, such as sum-marisation, translation, question answering and text classification. By designing specific tasks to test what LLMs "know," researchers can uncover insights into the models' representations, linguistic knowledge, and reasoning capabilities. Large Language Models (LLMs) might hallucinate facts, while curated Knowledge Graph (KGs) are typically factually reliable especially with domain-specific knowledge. These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data. Consequently, their probing results are prone to inflation or bias since models exhibit spurious correlations towards specific prompts [Poerner et al. Nov 27, 2025 · Abstract Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. One key reason for its success is the preservation of pre-trained features, achieved by obtaining a near-optimal linear head during LP. This paper presents ProPILE, a novel probing tool designed to empower data Jun 16, 2024 · As Large Language Models (LLMs) are deployed and integrated into thousands of applications, the need for scalable evaluation of how models respond to adversarial attacks grows rapidly. However, LLM security is a moving target: models produce unpredictable output, are con-stantly updated, and the potential adversary is highly diverse: anyone with access to the internet and a decent command of natural language. In this work, we probe LLMs from a human behavioral perspective, correlating values from LLMs with eye-tracking measures Jun 1, 2025 · In this work, we introduce graph probing, a method for uncovering the functional connectivity topology of LLM neurons and relating it to language generation performance. However, how the content of the prompts affects the model’s understanding Oct 22, 2024 · AI Safety: Classifying Large Language Models (LLMs) Exploits Rainbow Team Playbook: Understanding the top 10 LLM Exploitation Techniques. Apr 4, 2022 · Probing classifiers have emerged as one of the prominent methodologies for interpreting and analyzing deep neural network models of natural language processing. Abstract Probing techniques for large language mod-els (LLMs) have primarily focused on English, overlooking the vast majority of other world’s languages. The meta-reviewer recommends acceptance as a spotlight, suggesting the addition of concrete examples to enhance the Jan 26, 2024 · Papers Papers DyVal 2: Dynamic Evaluation of Large Language Models by Meta Probing Agents PromptBench: a unified library for evaluation of large language models DyVal: graph-informed dynamic evaluation of large language models Meta Semantic Template for Evaluation of Large Language Models A survey on evaluation of large language models These inquiries necessitate multifaceted exploration. We critically examine the evolution from Recurrent Neural Networks (RNNs) to Transformer models, highlighting the significant advancements and innovations in LLM architectures. These mechanisms can be leveraged to see what the model knows about different subjects and possibly to correct false information it has stored. Nov 1, 2023 · Large language models (LLMs) are believed to contain vast knowledge. They found that it forms a representation of program semantics to generate correct instructions. LLMs are typically evaluated using accuracy, yet this metric does not capture the vulnerability of LLMs to hallucination-inducing factors like prompt and context variability. Jun 3, 2024 · Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchmark datasets in the pre-training phase. May 29, 2025 · Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external context, but retrieved passages are often lengthy, noisy, or exceed input limits. While they provide valuable insights and assist in problem-solving, they can also potentially serve as a resource for malicious activities. Abstract As Large Language Models (LLMs) are de-ployed and integrated into thousands of appli-cations, the need for scalable evaluation of how models respond to adversarial attacks grows rapidly. Recent studies focus on the generated texts and compute 2 days ago · Abstract Large Language Models (LLMs) have shown their impressive capabilities, while also raising concerns about the data contamination problems due to privacy issues and leakage of benchmark datasets in the pre-training phase. Oct 15, 2023 · Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. Abstract Large Language Models (LLMs) often en-counter conflicts between their learned, in-ternal (parametric knowledge, PK) and exter-nal knowledge provided during inference (con-textual knowledge, CK). This paper presents ProPILE, a novel probing tool designed to empower Aug 20, 2024 · Large Language Models (LLMs) are implicit troublemakers. Large Language Model (LLM) deployment and integration comes with a need for scalable evaluation of how these mod-els respond to adversarial attacks. The proposal sets out a plan to examine the proficiency of LLMs in deciphering both explicit and implicit non Abstract The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). Understanding how LLMs models prioritize one knowledge source over the other remains a challenge. We use probing techniques🔍 on each layer's embedding to detect the layer accuracy, F1-score, and AUC of the classification task. Figure 1: Probing a Large Language Model (LLM) through the input of gesture descriptions can serve as a valuable means to evaluate its understanding of gestures, contributing to the refinement of human-AI interaction. There-fore, it is vital to detect the contamination by checking whether an LLM has been pre-trained on the target texts. Further, what Jun 16, 2024 · Abstract As Large Language Models (LLMs) are deployed and integrated into thousands of applications, the need for scalable evaluation of how models respond to adversarial attacks grows rapidly. However, recent studies have demonstrated Jul 4, 2023 · The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). Then, we use the intuition that an LLM knows a fact if the prompt’s uncertainty remains the same after instilling that fact into the model to introduce our measurements. What are Probing Tasks? Probing tasks are carefully designed tests to evaluate specific properties of an LLM Sep 22, 2024 · We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. Red Team, Blue Team, Tiger Team, Our Team: Evaluation & … Feb 28, 2024 · Abstract The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). Appropriate prompts can stimulate the knowledge capabilities of the model to solve different tasks. Using probing techniques, analyzing the models at different layers and stages of extracting interpretable features. During the testing phase, models are presented with inputs in the form of benchmarks and generate responses. The proposed research complements ongoing efforts exploring these questions. However, how neurons functionally co-activate with each other to give rise to emer-gent capabilities remains largely unknown, hindering a deeper understanding and safer development of LLMs. By training a linear classifier on model activations, our experiments reveal that certain activations correlate with determining whether context or parametric knowledge predominates in the generated outputs. Therefore, it is vital to detect the contamination by checking whether an LLM has been pre-trained on the target texts. Our findings reveal that LLMs exhibit a similar prediction pattern with humans but distinct from that of Shallow Language Models (SLMs). Jan 31, 2025 · Abstract Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of other world’s languages. In this paper, we extend these prob-ing methods to a multilingual context, investi-gating the behaviors of LLMs across diverse languages. LLM Graph Probing Graph probing is a tool for learning the functional connectivity topology of neurons in large language models (LLMs) and relating it to language generation performance. Nov 24, 2025 · Our study leverages intrinsic probing techniques, which identify which subsets of neurons encode linguistic features, to correlate the degree of cross-lingual neuron overlap with the zero-shot cross-lingual transfer performance for a given model. The project was initially described in the paper "Learning Neural Topology of Language Models with Graph Probing". The project delves into the Llama-2-7B model to understand the mechanics behind its language understanding capabilities. Sparse autoencoders (SAEs) linearize LLM embeddings, creating m … Feb 25, 2024 · In this paper, we propose a novel framework for explaining the layer-wise capability of large language models in encoding context knowledge via the probing task. In this work, we introduce Feb 17, 2024 · The behavior of a language model is confined to generating sequences of tokens, or, for the bare-bones LLM, generating distributions over tokens. Abstract Large Language Models (LLMs) are in-creasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. Jan 31, 2024 · The rise of Large Language Models (LLMs) has affected various disciplines that got beyond mere text generation. Measuring the alignment between KGs and LLMs can effectively probe the factualness and identify the knowledge blind spots of LLMs. Still No Lie Detector for Language Models: Probing Empirical and Conceptual Roadblocks [arxiv 2307] Position Paper: Toward New Frameworks for Studying Model Representations [arxiv 2402] Jun 1, 2025 · By probing models across diverse LLM families and scales, we discover a universal predictability of next-token prediction performance using only neural topology, which persists even when retaining just 1% of neuron connections. 07066v7 [cs. By doing so, we gain a deeper understanding of how these AI models function, bringing us closer to creating not only more powerful, but also more transparent AI systems. However Sep 10, 2024 · In this research, we introduce the Logic Tensor Probe (LTP), tailored specifically for assessing the reasoning capabilities of Large Language Models (LLMs). We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across . Implementing safety alignment could mitigate the risk of LLMs generating harmful responses. Despite advances in models like GPT-4o, their performance in complex driving environments remains largely unexplored. This paper presents ProPILE, a novel probing tool designed to empower Probing Language Models on Their Knowledge Source Overview This repository contains a framework for probing the knowledge source of language models. Our analyses suggests that LLMs are somewhat capable of answering causal queries the right way through memorization of the corresponding question-answer pair. By analyzing internal neural graphs across diverse LLM families and scales, we discover a universal predictability of next-token prediction performance using only neural topology. LMs are becoming increasingly important tools in Artificial Intelligence, because of the vast quantity of information they can store. , 2019) suggests that a linguistic hierarchy emerges in the LLM layers, with lower layers better suited to solving syntactic tasks and higher layers employed for semantic Abstract Purpose: Automatically identifying synonyms is an important but challenging aspect of entity normalization in knowledge graphs. However, LLM security is a moving target: models produce unpredictable output, are constantly updated, and the potential adversary is highly diverse: anyone with access to the internet and a decent command of Apr 26, 2024 · Large language models (LLMs) exhibit excellent ability to understand human languages, but do they also understand their own language that appears gibberish to us? In this work we delve into this question, aiming to uncover the mechanisms underlying such behavior in LLMs. In this paper, we investigate this hypothesis for PLMs, by probing metaphoricity information in their encodings, and by measuring the cross-lingual and cross-dataset generalization of this information. Both of these lack the full depth and breadth of human action. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across Abstract Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural representations to interpretable semantics. While noting the novel probing tool and well-crafted presentation, they encourage a broader evaluation and clearer definition of soft prompt parameters. Despite these points, probing offers valuable insights into the internal knowledge structures learned by large language models. However, there is no certainty whether these explanations are reliable and reflect the arXiv:2404. Existing model interpretability research (Tenney et al. Existing compression methods typically require supervised training of dedicated compression models, increasing cost and reducing portability. Recent studies focus on the generated texts 2 days ago · Large pre-trained language models (PLMs) are therefore assumed to encode metaphorical knowledge useful for NLP systems. We argue that: even when an LLM appears to successfully block harmful queries, there may still be hidden By examining the probability distribution of language models over the vocabulary when predicting a blank, we first introduce the concept of prompt uncertainty. The review explores state-of-the Aug 14, 2024 · An MIT team used probing classifiers to investigate if language models trained only on next-token prediction can capture the underlying meaning of programming languages. May 27, 2024 · The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. However, LLM security is a moving target: models produce unpredictable output, are constantly updated, and the potential adversary is highly diverse: anyone with access to the internet and a decent Jul 2, 2025 · 1 Introduction Currently, the lifecycle of a Large Language Model (LLM) typically involves four phases: pretraining, posttraining, testing, and deployment. We aim to investigate the potential of pre-trained large language models (LLMs) for this task. This paper presents ProPILE, a novel probing tool designed to empower data subjects, or the owners of the PII, with awareness of potential PII leakage in LLM-based services. , 2020]. Knowledge Probing with Large Language Models This is a repository for knowledge probing for large language models, which is part of the paper "Why Do Neural Language Models Still Need Commonsense Knowledge?" and "Impact of Co-occurrence on Factual Knowledge of Large Language Models" (EMNLP 2023 Findings) (project page). Our experimental study assesses various MLLMs as world models using in-car Contribute to hy-zhao23/Explainability-for-Large-Language-Models development by creating an account on GitHub. Many works have extended LLMs to multimodal models and applied them to various multimodal downstream tasks with a unified model structure using prompt.