Do LLMs Dream of Ontologies?

• URL: http://arxiv.org/abs/2401.14931v1
• Date: Fri, 26 Jan 2024 15:10:23 GMT
• Title: Do LLMs Dream of Ontologies?
• Authors: Marco Bombieri, Paolo Fiorini, Simone Paolo Ponzetto, Marco Rospocher
• Abstract summary: Large language models (LLMs) have recently revolutionized automated text understanding and generation. This paper investigates whether and to what extent general-purpose pre-trained LLMs have information from known.
• Score: 15.049502693786698
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) have recently revolutionized automated text understanding and generation. The performance of these models relies on the high number of parameters of the underlying neural architectures, which allows LLMs to memorize part of the vast quantity of data seen during the training. This paper investigates whether and to what extent general-purpose pre-trained LLMs have memorized information from known ontologies. Our results show that LLMs partially know ontologies: they can, and do indeed, memorize concepts from ontologies mentioned in the text, but the level of memorization of their concepts seems to vary proportionally to their popularity on the Web, the primary source of their training material. We additionally propose new metrics to estimate the degree of memorization of ontological information in LLMs by measuring the consistency of the output produced across different prompt repetitions, query languages, and degrees of determinism.

Related papers

• Undesirable Memorization in Large Language Models: A Survey [5.659933808910005]
  We present a Systematization of Knowledge (SoK) on the topic of memorization in Large Language Models (LLMs) Memorization is the effect that a model tends to store and reproduce phrases or passages from the training data. We discuss the metrics and methods used to measure memorization, followed by an analysis of the factors that contribute to memorization phenomenon.
  arXiv  Detail & Related papers  (2024-10-03T16:34:46Z)
• Scaling Laws for Fact Memorization of Large Language Models [67.94080978627363]
  We analyze the scaling laws for Large Language Models' fact knowledge and their behaviors of memorizing different types of facts. We find that LLMs' fact knowledge capacity has a linear and negative exponential law relationship with model size and training epochs. Our findings reveal the capacity and characteristics of LLMs' fact knowledge learning, which provide directions for LLMs' fact knowledge augmentation.
  arXiv  Detail & Related papers  (2024-06-22T03:32:09Z)
• A Multi-Perspective Analysis of Memorization in Large Language Models [10.276594755936529]
  Large Language Models (LLMs) show unprecedented performance in various fields. LLMs can generate the same content used to train them. This research comprehensively discussed memorization from various perspectives.
  arXiv  Detail & Related papers  (2024-05-19T15:00:50Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• Enabling Large Language Models to Learn from Rules [99.16680531261987]
  We are inspired that humans can learn the new tasks or knowledge in another way by learning from rules. We propose rule distillation, which first uses the strong in-context abilities of LLMs to extract the knowledge from the textual rules. Our experiments show that making LLMs learn from rules by our method is much more efficient than example-based learning in both the sample size and generalization ability.
  arXiv  Detail & Related papers  (2023-11-15T11:42:41Z)
• SoK: Memorization in General-Purpose Large Language Models [25.448127387943053]
  Large Language Models (LLMs) are advancing at a remarkable pace, with myriad applications under development. LLMs can memorize short secrets in the training data, but can also memorize concepts like facts or writing styles that can be expressed in text in many different ways. We propose a taxonomy for memorization in LLMs that covers verbatim text, facts, ideas and algorithms, writing styles, distributional properties, and alignment goals.
  arXiv  Detail & Related papers  (2023-10-24T14:25:53Z)
• Quantifying and Analyzing Entity-level Memorization in Large Language Models [4.59914731734176]
  Large language models (LLMs) have been proven capable of memorizing their training data. Privacy risks arising from memorization have attracted increasing attention. We propose a fine-grained, entity-level definition to quantify memorization with conditions and metrics closer to real-world scenarios.
  arXiv  Detail & Related papers  (2023-08-30T03:06:47Z)
• Give Us the Facts: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling [34.59678835272862]
  ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities. This paper proposes to enhance LLMs with knowledge graph-enhanced large language models (KGLLMs) KGLLM provides a solution to enhance LLMs' factual reasoning ability, opening up new avenues for LLM research.
  arXiv  Detail & Related papers  (2023-06-20T12:21:06Z)
• RET-LLM: Towards a General Read-Write Memory for Large Language Models [53.288356721954514]
  RET-LLM is a novel framework that equips large language models with a general write-read memory unit. Inspired by Davidsonian semantics theory, we extract and save knowledge in the form of triplets. Our framework exhibits robust performance in handling temporal-based question answering tasks.
  arXiv  Detail & Related papers  (2023-05-23T17:53:38Z)
• The Web Can Be Your Oyster for Improving Large Language Models [98.72358969495835]
  Large language models (LLMs) encode a large amount of world knowledge. We consider augmenting LLMs with the large-scale web using search engine. We present a web-augmented LLM UNIWEB, which is trained over 16 knowledge-intensive tasks in a unified text-to-text format.
  arXiv  Detail & Related papers  (2023-05-18T14:20:32Z)

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.