Myeongjun Jang

Orcid: 0000-0002-9352-4799

Affiliations:
  • J. P. Morgan, London, UK
  • University of Oxford, UK (PhD 2025)
  • Korea University, Department of Industrial Management Engineering, Seoul, South Korea (former)


According to our database1, Myeongjun Jang authored at least 23 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Industry-Aligned Granular Topic Modeling.
CoRR, January, 2026

2025
InstaJudge: Aligning Judgment Bias of LLM-as-Judge with Humans in Industry Applications.
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

2024
Pre-training and diagnosing knowledge base completion models.
Artif. Intell., 2024

Leveraging Natural Language Processing and Large Language Models for Assisting Due Diligence in the Legal Domain.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, 2024

DriftWatch: A Tool that Automatically Detects Data Drift and Extracts Representative Examples Affected by Drift.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, 2024

2023
Consistency Analysis of ChatGPT.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Improving Language Models' Meaning Understanding and Consistency by Learning Conceptual Roles from Dictionary.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023

2022
NoiER: An Approach for Training More Reliable Fine-Tuned Downstream Task Models.
IEEE ACM Trans. Audio Speech Lang. Process., 2022

Sentence transition matrix: An efficient approach that preserves sentence semantics.
Comput. Speech Lang., 2022

KOBEST: Korean Balanced Evaluation of Significant Tasks.
CoRR, 2022

Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence.
Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

BECEL: Benchmark for Consistency Evaluation of Language Models.
Proceedings of the 29th International Conference on Computational Linguistics, 2022

KoBEST: Korean Balanced Evaluation of Significant Tasks.
Proceedings of the 29th International Conference on Computational Linguistics, 2022

2021
Are Training Resources Insufficient? Predict First Then Explain!
CoRR, 2021

NoiER: An Approach for Training more Reliable Fine-TunedDownstream Task Models.
CoRR, 2021

Accurate, yet inconsistent? Consistency Analysis on Language Understanding Models.
CoRR, 2021

Intrusion Detection Based on Sequential Information Preserving Log Embedding Methods and Anomaly Detection Algorithms.
IEEE Access, 2021

Learning-Free Unsupervised Extractive Summarization Model.
IEEE Access, 2021

2020
Corrigendum to "Recurrent neural network-based semantic variational autoencoder for Sequence-to-sequence learning" [Information Sciences 490 (2019) 59-73].
Inf. Sci., 2020

Paraphrase thought: Sentence embedding module imitating human language recognition.
Inf. Sci., 2020

Unusual customer response identification and visualization based on text mining and anomaly detection.
Expert Syst. Appl., 2020

2019
Recurrent neural network-based semantic variational autoencoder for Sequence-to-sequence learning.
Inf. Sci., 2019


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