COPEN: Probing Conceptual Knowledge in Pre-trained Language Models

• URL: http://arxiv.org/abs/2211.04079v1
• Date: Tue, 8 Nov 2022 08:18:06 GMT
• Title: COPEN: Probing Conceptual Knowledge in Pre-trained Language Models
• Authors: Hao Peng, Xiaozhi Wang, Shengding Hu, Hailong Jin, Lei Hou, Juanzi Li, Zhiyuan Liu, Qun Liu
• Abstract summary: Conceptual knowledge is fundamental to human cognition and knowledge bases. Existing knowledge probing works only focus on factual knowledge of pre-trained language models (PLMs) and ignore conceptual knowledge. We design three tasks to probe whether PLMs organize entities by conceptual similarities, learn conceptual properties, and conceptualize entities in contexts. For the tasks, we collect and annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual knowledge Probing bENchmark.
• Score: 60.10147136876669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conceptual knowledge is fundamental to human cognition and knowledge bases. However, existing knowledge probing works only focus on evaluating factual knowledge of pre-trained language models (PLMs) and ignore conceptual knowledge. Since conceptual knowledge often appears as implicit commonsense behind texts, designing probes for conceptual knowledge is hard. Inspired by knowledge representation schemata, we comprehensively evaluate conceptual knowledge of PLMs by designing three tasks to probe whether PLMs organize entities by conceptual similarities, learn conceptual properties, and conceptualize entities in contexts, respectively. For the tasks, we collect and annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual knowledge Probing bENchmark. Extensive experiments on different sizes and types of PLMs show that existing PLMs systematically lack conceptual knowledge and suffer from various spurious correlations. We believe this is a critical bottleneck for realizing human-like cognition in PLMs. COPEN and our codes are publicly released at https://github.com/THU-KEG/COPEN.

Related papers

• 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)
• Reasoning about concepts with LLMs: Inconsistencies abound [13.042591838719936]
  Large language models (LLMs) often display and demonstrate significant inconsistencies in their knowledge. In particular, we have been able to significantly enhance the performance of LLMs of various sizes with openly available weights.
  arXiv  Detail & Related papers  (2024-05-30T15:38:54Z)
• ConcEPT: Concept-Enhanced Pre-Training for Language Models [57.778895980999124]
  ConcEPT aims to infuse conceptual knowledge into pre-trained language models. It exploits external entity concept prediction to predict the concepts of entities mentioned in the pre-training contexts. Results of experiments show that ConcEPT gains improved conceptual knowledge with concept-enhanced pre-training.
  arXiv  Detail & Related papers  (2024-01-11T05:05:01Z)
• Large Knowledge Model: Perspectives and Challenges [37.42721596964844]
  emphLarge Language Models (LLMs) epitomize the pre-training of extensive, sequence-based world knowledge into neural networks. This article explores large models through the lens of "knowledge" Considering the intricate nature of human knowledge, we advocate for the creation of emphLarge Knowledge Models (LKM)
  arXiv  Detail & Related papers  (2023-12-05T12:07:30Z)
• Knowledge Rumination for Pre-trained Language Models [77.55888291165462]
  We propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize related latent knowledge without retrieving it from the external corpus. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, and GPT-3.
  arXiv  Detail & Related papers  (2023-05-15T15:47:09Z)
• Causality-aware Concept Extraction based on Knowledge-guided Prompting [17.4086571624748]
  Concepts benefit natural language understanding but are far from complete in existing knowledge graphs (KGs) Recently, pre-trained language models (PLMs) have been widely used in text-based concept extraction. We propose equipping the PLM-based extractor with a knowledge-guided prompt as an intervention to alleviate concept bias.
  arXiv  Detail & Related papers  (2023-05-03T03:36:20Z)
• Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization [49.00409552570441]
  We study the role of conceptualization in commonsense reasoning, and formulate a framework to replicate human conceptual induction. We apply the framework to ATOMIC, a large-scale human-annotated CKG, aided by the taxonomy Probase.
  arXiv  Detail & Related papers  (2022-06-03T12:24:49Z)
• KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models [28.82149012250609]
  We propose a benchmark, named Knowledge Memorization, Identification, and Reasoning test (KMIR) KMIR covers 3 types of knowledge, including general knowledge, domain-specific knowledge, and commonsense, and provides 184,348 well-designed questions. Preliminary experiments with various representative pre-training language models on KMIR reveal many interesting phenomenons.
  arXiv  Detail & Related papers  (2022-02-28T03:52:57Z)
• A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base [8.591839265985412]
  Acquiring commonsense knowledge and reasoning is recognized as an important frontier in achieving general Artificial Intelligence (AI) In this paper, we propose and conduct a systematic study to enable a deeper understanding of commonsense knowledge by doing an empirical and structural analysis of the ConceptNet knowledge base. Detailed experimental results on three carefully designed research questions, using state-of-the-art unsupervised graph representation learning ('embedding') and clustering techniques, reveal deep substructures in ConceptNet relations.
  arXiv  Detail & Related papers  (2020-11-28T08:08:25Z)
• Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers [54.417299589288184]
  We investigate models for complementing the distributional knowledge of BERT with conceptual knowledge from ConceptNet and its corresponding Open Mind Common Sense (OMCS) corpus. Our adapter-based models substantially outperform BERT on inference tasks that require the type of conceptual knowledge explicitly present in ConceptNet and OMCS.
  arXiv  Detail & Related papers  (2020-05-24T15:49:57Z)

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.