Quyet V. Do

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20222026

most citedPseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population

1 citations · 2 across the 3 of their papers we have counts for

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cs.CL2026

$π^{2}$ : Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models

Quyet V. Do, Thinh Pham, Nguyen Nguyen +3

We study a pipeline that curates reasoning data from initial structured data for improving long-context reasoning in large language models (LLMs). Our approach, $π^{2}$ , constructs h…

cs.CL2024★ 1 cited

What Really is Commonsense Knowledge?

Quyet V. Do, Junze Li, Tung-Duong Vuong +3

Commonsense datasets have been well developed in Natural Language Processing, mainly through crowdsource human annotation. However, there are debates on the genuineness of commonse…

cs.CL2022★ 1 cited

PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population

Tianqing Fang, Quyet V. Do, Hongming Zhang +3

Commonsense Knowledge Base (CSKB) Population aims at reasoning over unseen entities and assertions on CSKBs, and is an important yet hard commonsense reasoning task. One challenge…

π2: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models

What Really is Commonsense Knowledge?

PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population

$π^{2}$ : Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models