33 citations · 102 across the 13 of their papers we have counts for
13 papers
REAP: Relation-Aware Elicitation and Parsing for Closed-Book Knowledge Base Construction from LLMs
Thanh-Dan Bui, Thanh-Trung Do, Tuan-Phong Nguyen
We present the REAP system for the AKBC Shared Task 2026 on constructing knowledge bases from language models in a closed-book setting, subject to a budget of at most 32B parameter…
GPTKB 2.0: Browsing, Querying, and Auditing a Disambiguated LLM-Derived Knowledge Base
Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM). GPTKB 2.0 contains 38.4M triples over 1.6M canon…
Constructing Disambiguated Knowledge Bases from Large Language Models at Scale
Yujia Hu, Tuan-Phong Nguyen, Simon Razniewski
Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large language models (LLMs), treating the model…
Auditing a KB Elicitation of Frontier LLM Knowledge: A Multi-dimensional Analysis of GPTKB v1.5
Shrestha Ghosh, Luca Giordano, Yujia Hu +2
LLMs are remarkable artifacts that have revolutionized a range of knowledge-intensive tasks. A significant contributor is their factual knowledge, which, to date, remains poorly un…
GPTKB v1.5: A Massive Knowledge Base for Exploring Factual LLM Knowledge
Yujia Hu, Tuan-Phong Nguyen, Shrestha Ghosh +2
Language models are powerful artifacts, yet their factual knowledge is still poorly understood, and inaccessible to ad-hoc browsing and scalable statistical analysis. This demonstr…
Enabling LLM Knowledge Analysis via Extensive Materialization
Yujia Hu, Tuan-Phong Nguyen, Shrestha Ghosh +1
Large language models (LLMs) have majorly advanced NLP and AI, and next to their ability to perform a wide range of procedural tasks, a major success factor is their internalized f…
Cultural Commonsense Knowledge for Intercultural Dialogues
Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
Despite recent progress, large language models (LLMs) still face the challenge of appropriately reacting to the intricacies of social and cultural conventions. This paper presents…
Extracting Cultural Commonsense Knowledge at Scale
Tuan-Phong Nguyen, Simon Razniewski, Aparna Varde +1
Structured knowledge is important for many AI applications. Commonsense knowledge, which is crucial for robust human-centric AI, is covered by a small number of structured knowledg…
An Experimental Investigation of Part-Of-Speech Taggers for Vietnamese
Tuan-Phong Nguyen, Quoc-Tuan Truong, Xuan-Nam Nguyen +1
Part-of-speech (POS) tagging plays an important role in Natural Language Processing (NLP). Its applications can be found in many NLP tasks such as named entity recognition, syntact…
Materialized Knowledge Bases from Commonsense Transformers
Tuan-Phong Nguyen, Simon Razniewski
Starting from the COMET methodology by Bosselut et al. (2019), generating commonsense knowledge directly from pre-trained language models has recently received significant attentio…
Refined Commonsense Knowledge from Large-Scale Web Contents
Tuan-Phong Nguyen, Simon Razniewski, Julien Romero +1
Commonsense knowledge (CSK) about concepts and their properties is helpful for AI applications. Prior works, such as ConceptNet, have compiled large CSK collections. However, they…
Inside ASCENT: Exploring a Deep Commonsense Knowledge Base and its Usage in Question Answering
Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
ASCENT is a fully automated methodology for extracting and consolidating commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances traditional triple-based co…
Advanced Semantics for Commonsense Knowledge Extraction
Tuan-Phong Nguyen, Simon Razniewski, Gerhard Weikum
Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled larg…