Unveiling LLMs: The Evolution of Latent Representations in a Temporal Knowledge Graph

• URL: http://arxiv.org/abs/2404.03623v1
• Date: Thu, 4 Apr 2024 17:45:59 GMT
• Title: Unveiling LLMs: The Evolution of Latent Representations in a Temporal Knowledge Graph
• Authors: Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini,
• Abstract summary: Large Language Models (LLMs) demonstrate an impressive capacity to recall a vast range of common factual knowledge information. We propose an end-to-end framework that jointly decodes the factual knowledge embedded in the latent space of LLMs. We showcase our framework with local and global interpretability analyses using two claim verification datasets.
• Score: 15.129079475322637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) demonstrate an impressive capacity to recall a vast range of common factual knowledge information. However, unravelling the underlying reasoning of LLMs and explaining their internal mechanisms of exploiting this factual knowledge remain active areas of investigation. Our work analyzes the factual knowledge encoded in the latent representation of LLMs when prompted to assess the truthfulness of factual claims. We propose an end-to-end framework that jointly decodes the factual knowledge embedded in the latent space of LLMs from a vector space to a set of ground predicates and represents its evolution across the layers using a temporal knowledge graph. Our framework relies on the technique of activation patching which intervenes in the inference computation of a model by dynamically altering its latent representations. Consequently, we neither rely on external models nor training processes. We showcase our framework with local and global interpretability analyses using two claim verification datasets: FEVER and CLIMATE-FEVER. The local interpretability analysis exposes different latent errors from representation to multi-hop reasoning errors. On the other hand, the global analysis uncovered patterns in the underlying evolution of the model's factual knowledge (e.g., store-and-seek factual information). By enabling graph-based analyses of the latent representations, this work represents a step towards the mechanistic interpretability of LLMs.

Related papers

• Unveiling Knowledge Utilization Mechanisms in LLM-based Retrieval-Augmented Generation [77.10390725623125]
  retrieval-augmented generation (RAG) is widely employed to expand their knowledge scope.<n>Since RAG has shown promise in knowledge-intensive tasks like open-domain question answering, its broader application to complex tasks and intelligent assistants has further advanced its utility.<n>We present a systematic investigation of the intrinsic mechanisms by which RAGs integrate internal (parametric) and external (retrieved) knowledge.
  arXiv  Detail & Related papers  (2025-05-17T13:13:13Z)
• Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws [5.685201910521295]
  We offer a detailed view of how Large Language Models acquire and store information across increasing model and data scales. Motivated by this theoretical perspective and natural assumptions inspired by Heap's and Zipf's laws, we introduce a simplified yet representative hierarchical data-generation framework. Under the Bayesian setting, we show that prediction and compression within this model naturally lead to diverse learning and scaling behaviors.
  arXiv  Detail & Related papers  (2025-04-13T14:31:52Z)
• How LLMs Learn: Tracing Internal Representations with Sparse Autoencoders [30.36521888592164]
  Large Language Models (LLMs) demonstrate remarkable multilingual capabilities and broad knowledge. We analyze how the information encoded in LLMs' internal representations evolves during the training process.
  arXiv  Detail & Related papers  (2025-03-09T02:13:44Z)
• KaLM: Knowledge-aligned Autoregressive Language Modeling via Dual-view Knowledge Graph Contrastive Learning [74.21524111840652]
  This paper proposes textbfKaLM, a textitKnowledge-aligned Language Modeling approach. It fine-tunes autoregressive large language models to align with KG knowledge via the joint objective of explicit knowledge alignment and implicit knowledge alignment. Notably, our method achieves a significant performance boost in evaluations of knowledge-driven tasks.
  arXiv  Detail & Related papers  (2024-12-06T11:08:24Z)
• Beyond Sight: Towards Cognitive Alignment in LVLM via Enriched Visual Knowledge [24.538839144639653]
  Large Vision-Language Models (LVLMs) integrate separately pre-trained vision and language components. These models frequently encounter a core issue of "cognitive misalignment" between the vision encoder (VE) and the large language model (LLM)
  arXiv  Detail & Related papers  (2024-11-25T18:33:14Z)
• Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs [55.317267269115845]
  Chain-of-Knowledge (CoK) is a comprehensive framework for knowledge reasoning. CoK includes methodologies for both dataset construction and model learning. We conduct extensive experiments with KnowReason.
  arXiv  Detail & Related papers  (2024-06-30T10:49:32Z)
• LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements [59.71218039095155]
  Task of reading comprehension (RC) provides a primary means to assess language models' natural language understanding (NLU) capabilities. If the context aligns with the models' internal knowledge, it is hard to discern whether the models' answers stem from context comprehension or from internal information. To address this issue, we suggest to use RC on imaginary data, based on fictitious facts and entities.
  arXiv  Detail & Related papers  (2024-04-09T13:08:56Z)
• Towards Uncovering How Large Language Model Works: An Explainability Perspective [38.07611356855978]
  Large language models (LLMs) have led to breakthroughs in language tasks, yet the internal mechanisms that enable their remarkable generalization and reasoning abilities remain opaque. This paper aims to uncover the mechanisms underlying LLM functionality through the lens of explainability.
  arXiv  Detail & Related papers  (2024-02-16T13:46:06Z)
• From Understanding to Utilization: A Survey on Explainability for Large Language Models [27.295767173801426]
  This survey underscores the imperative for increased explainability in Large Language Models (LLMs) Our focus is primarily on pre-trained Transformer-based LLMs, which pose distinctive interpretability challenges due to their scale and complexity. When considering the utilization of explainability, we explore several compelling methods that concentrate on model editing, control generation, and model enhancement.
  arXiv  Detail & Related papers  (2024-01-23T16:09:53Z)
• A Comprehensive Study of Knowledge Editing for Large Language Models [82.65729336401027]
  Large Language Models (LLMs) have shown extraordinary capabilities in understanding and generating text that closely mirrors human communication. This paper defines the knowledge editing problem and provides a comprehensive review of cutting-edge approaches. We introduce a new benchmark, KnowEdit, for a comprehensive empirical evaluation of representative knowledge editing approaches.
  arXiv  Detail & Related papers  (2024-01-02T16:54:58Z)
• Is Knowledge All Large Language Models Needed for Causal Reasoning? [11.476877330365664]
  This paper explores the causal reasoning of large language models (LLMs) to enhance their interpretability and reliability in advancing artificial intelligence. We propose a novel causal attribution model that utilizes do-operators" for constructing counterfactual scenarios.
  arXiv  Detail & Related papers  (2023-12-30T04:51:46Z)
• Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention [53.896974148579346]
  Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains. The enigmatic black-box'' nature of LLMs remains a significant challenge for interpretability, hampering transparent and accountable applications. We propose a novel methodology anchored in sparsity-guided techniques, aiming to provide a holistic interpretation of LLMs.
  arXiv  Detail & Related papers  (2023-12-22T19:55:58Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations [11.008412414253662]
  Large Language Models (LLMs) encode meanings of words in the form of distributed semantics. Recent studies have shown that LLMs tend to generate unintended, inconsistent, or wrong texts as outputs. We propose a novel ensemble learning method, Interpretable Ensemble Representation Learning (IERL), that systematically combines LLM and crowdsourced knowledge representations.
  arXiv  Detail & Related papers  (2023-06-24T05:02:34Z)
• Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation [68.13453771001522]
  We propose a multimodal intensive ZSL framework that matches regions of images with corresponding semantic embeddings. We conduct extensive experiments and evaluate our model on large-scale real-world data.
  arXiv  Detail & Related papers  (2023-06-14T13:07:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.