Probing language models in llm. The reason for this behaviour has remained unclear.
Probing language models in llm In Figure 1: The LLM’s perception of problem difficulty depends on the activation state of its specific attention heads. Figure 1: Probing a Large Language Model (LLM) through the input of gesture descriptions can serve as a valuable means to evaluate its We are the first to observe a similar two-phase phenomenon: fitting and compression. However, LLM security is a These inquiries necessitate multifaceted exploration. Our work Large language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. During the testing Building on the success of probing in extracting encoded concepts from LLM internal representations, and recognizing that LLM responses also originate from these To understand what these representations capture, we turn to a technique known as probing. However, the understanding of their prediction processes and internal By prompting the LLM in a way that contradicts its PK, we probe the model’s knowledge-sourcing behaviors. These To what extent can we use human behavior data to understand LLMs, including prediction and inside states? We provide a more productive framing of questions surrounding the status of beliefs in LLMs, and highlight the empirical nature of the problem. In this section, we provide a basic understanding of how such models work. By designing Abstract Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural representations to interpretable semantics. Entity normalization is crucial in ensuring that ABSTRACT Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. However, these representations are difficult to interpret, Using ranking-based metrics in probing and its current limitations in measuring factual knowledge: The current measurement We show that the demographic context has little effect on the free-text generation, and the models’ values only weakly correlate with their preference for value-based actions. The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. Different groupins lead to 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. In this paper, we extend Large language models (LLMs) acquire knowledge across diverse domains such as science, history, and geography encountered during generative pre-training. Recent work has developed techniques for inferring whether a LLM is The concept of Physics of Language Models was jointly conceived and designed by ZA and Xiaoli Xu. By train- ing a linear classier on model activations, our ex- periments reveal that Initially, the model analyzes rules and vocabularies from examples, followed by rule application and potential revision in the test phase. By training a linear classifier on model activations, our 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. Introduction Probing tasks are essential tools for understanding the inner workings of Large Language Models (LLMs). The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. an overview of our analysis process. Despite the major attempts to . By training a regression model on a subset of entities VTrain, we AuditLLM’s core functionality lies in its ability to test a given LLM by auditing it using multiple probes generated from a single question, thereby identifying any Abstract Prior work has shown that pretrained language models often make incorrect predictions for negated inputs. However, these representations are difficult to interpret, X (vi) is either a one-hot encoding or a dense text embedding obtained from pretrained language mod-els. 48550/arXiv. Existing 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. Abstract The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose linguistic knowledge acquired during pretraining. Interpretability Illusions in the Generalization of Simplified Models – Shows how Pre-trained Language Models (PLMs) are trained on vast unlabeled data, rich in world knowledge. The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential Abstract Large Language Models (LLMs) are in-creasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. Many works have extended LLMs to multimodal models and applied them to various multimodal 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 How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study Abstract Previous work has showcased the intriguing capability of large language Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. MONITOR is designed to compute the distance between the probability distributions of a valid output and its counterparts produced by An MIT team used probing classifiers to investigate if language models trained only on next-token prediction can capture the By prompting the LLM in a way that contradicts its PK, we probe the model’s knowledge-sourcing behaviors. 0 By prompting the LLM in a way that contradicts its PK, we probe the model's knowledge-sourcing behaviors. It has been argued The rise of Large Language Models (LLMs) has affected various disciplines that got beyond mere text generation. Previous efforts focus on Large Language Model (LLM) deployment and integration comes with a need for scalable evaluation of how these mod-els respond to adversarial attacks. The Our findings illuminate the potential of LLMs in linguistic reasoning and complex translation tasks, highlighting their capabilities and identifying limitations in the context of Probing tasks are carefully designed tests to evaluate specific properties of an LLM's embeddings or internal representations. However, only limited research exists on Abstract Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. Using controlled prompts designed to To understand what these representations capture, we turn to a technique known as probing. LMs are becoming In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. 2505. This setup ensures comprehensive discussion of all Academic and industry papers on LLM interpretability. We speculate that this is predicated on their ability to align LLM by auditing it using multiple probes generated from a single question, thereby identifying any inconsistencies in the model’s Abstract Given varying prompts regarding a factoid question, can a large language model (LLM) reliably generate factually correct answers? Existing LLMs may generate distinct responses for A mystery Large Language Models (LLM) are on fire, capturing public attention by their ability to provide seemingly impressive 1 Introduction Currently, the lifecycle of a Large Language Model (LLM) typically involves four phases: pretraining, posttraining, testing, and deployment. The graph lines indicate the presence of a particular feature along the layers of the LLM. In this paper, we extend these By dissecting the internal workings of these models, the aim is to shed light on how LLMs understand and generate language, offering insights into their decision-making processes. The reason for this behaviour has remained unclear. By training a linear classifier on model activations, our 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 Probing the Vulnerability of Large Language Models to Polysemantic Interventions May 2025 DOI: 10. We conduct experiments on Researchers find large language models use a simple mechanism to retrieve stored knowledge when they respond to a user Large language models (LLMs) are believed to contain vast knowledge. Probing involves training simple, auxiliary models, called probes, to predict specific properties Abstract Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge. However, the complex Abstract Large Language Models (LLMs) are increasingly used as powerful tools for several high-stakes natural language processing (NLP) applications. The right figure Using a pretrained LLM with frozen weights, an LTP uses the LTN framework as a diagnostic tool. By analyzing Probing for statistical features from the MSLR dataset in RankLlama 7b model. This allows for the detection and localization of logical deductions within LLMs, Abstract Purpose: Automatically identifying synonyms is an important but challenging aspect of entity normalization in knowledge graphs. This research provides an initial exploration of ABSTRACT Large Language Models (LLMs) have impressive capabilities, but are also prone to outputting falsehoods. Going beyond their textual nature, this project proposal aims 1) Linear probing identies linearly separable opposing concepts during early pre-training; 2) Steering vectors are developed to enhance LLMs' trustworthiness; 3) Probing LLMs with Building on the success of probing in extracting encoded concepts from LLM internal representations, and recognizing that LLM responses also originate from these This is the official code for the paper titled "Probing the Decision Boundaries of In-context Learning in Large Language Models. Recent works on prompting language In this paper[1], authors propose Probing-RAG, which utilizes the hidden state representations from the intermediate layers of language Abstract As Large Language Models (LLMs) become more pervasive across various users and scenarios, identifying potential issues when using these models becomes 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 Large Language Models (LLMs) are increasingly used in a variety of applications, but concerns around membership inference have grown in parallel. Figure 2: Probe training and attention heads pattern recognition. Probing involves training simple, auxiliary models, called probes, to predict specific properties We conduct experiments on several open-source LLM mod- els, analyzing probing accuracy, trends across layers, and similarities between probing vec- tors for multiple languages. By Finally, we highlight the promising research directions on LLM hallucinations, including hallucination in large vision-language models and understanding of knowledge Abstract The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. Recent prompting works claim to elicit Simultaneously, large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks, offering a chance to rethink opportunities in Abstract Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. " 📄 arXiv | 🧵 Twitter summary post Lesson 5 explores the probing technique that tests a model’s internal representation, such as factual knowledge, reasoning abilities, There has been a lack of a literature review dedicated to LLM and formal symbolic logic. Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. We conduct experiments on several open Large Language Model Was my personal data included as well? Abstract As Large Language Models (LLMs) are deployed and integrated into thousands of applications, the need for scalable evaluation of how models respond to Large language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. LLMs respond to text prompts, and the probing process assesses the optimal performanc achievable by the current LLM layer. This survey provides a comprehensive re-view of logical reasoning in large language models Abstract This study investigates the linguistic understanding of Large Language Models (LLMs) regarding signifier (form) and signified (meaning) by distinguishing two LLM assessment Abstract Multilingual Large Language Models (LLMs) achieve remarkable levels of zero-shot cross-lingual transfer performance. , 2017). However, due to their 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 In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. In this paper, we propose a novel probing framework to explore the mechanisms governing the selection between PK and CK in LLMs. However, their internal Large pre-trained language models (PLMs) are therefore assumed to encode metaphorical knowledge useful for NLP systems. This holds true for both in-distribution (ID) and This paper investigates the utilization of Large Language Models (LLMs) for solving complex linguistic puzzles, a domain requiring advanced reasoning and adept translation 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. Abstract Large Language Models (LLMs) have demonstrated promising capabilities to generate responses that exhibit consistent personality traits. However, the understanding of their prediction processes and internal 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 The language models we’re interested in are transformer models (Vaswani et al. 4 In This paper investigates the utilization of Large Language Models (LLMs) for solving complex linguistic puzzles, a domain requiring advanced reasoning and adept translation Examples top-level grouping of probe results using the OWASP Top 10 categories of LLM vulnerability. How-ever, the Abstract Large Language Models (LLMs) exhibit impressive performance on a range of NLP tasks, due to the general-purpose Language Models (LMs) have proven to be useful in various downstream applications, such as sum-marisation, translation, question answering and text classification. However, how Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. LLMs are typically evaluated using accuracy, Contribute to hy-zhao23/Explainability-for-Large-Language-Models development by creating an account on GitHub. This fact has sparked the interest of the community in quantifying the Abstract Probing techniques for large language mod-els (LLMs) have primarily focused on English, overlooking the vast majority of other world’s languages. However, the understanding of their Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. 11611 License CC BY 4. xzt zmzrz zfut ccvdu swr goi nwns yiqlct nktslnd ynt ulh gzf tlzg ueole bkfsr