Papers by Cheonbok Park
Towards Accurate Translation via Semantically Appropriate Application of Lexical Constraints (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing work has not evaluated LNMT models under challenging real-world conditions. |
| Approach: | They propose a homograph disambiguation module and a model that integrates contextually rich information about unseen lexical constraints from pre-trained language models. |
| Outcome: | The proposed model can cope with “homographs” and “unseen” lexical constraints. |
Adaptive Contrastive Decoding in Retrieval-Augmented Generation for Handling Noisy Contexts (2024.findings-emnlp)
Copied to clipboard
Youna Kim, Hyuhng Joon Kim, Cheonbok Park, Choonghyun Park, Hyunsoo Cho, Junyeob Kim, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Recent research has been developed to amplify contextual knowledge over parametric knowledge of large language models (LLMs) in knowledge-intensive tasks such as open-domain question-answering . |
| Approach: | They propose to amplify contextual knowledge over parametric knowledge of large language models (LLMs) by contrastive decoding to leverage contextual influence effectively. |
| Outcome: | The proposed approach improves open-domain question answering tasks especially in robustness by remaining undistracted by noisy contexts in retrieval-augmented generation. |
Aligning Language Models to Explicitly Handle Ambiguity (2024.emnlp-main)
Copied to clipboard
Hyuhng Joon Kim, Youna Kim, Cheonbok Park, Junyeob Kim, Choonghyun Park, Kang Min Yoo, Sang-goo Lee, Taeuk Kim
| Challenge: | Large language models (LLMs) are not specifically trained to deal with ambiguous utterances . ambiguity can lead to varying interpretations of the same input based on different assumptions or background knowledge . |
| Approach: | They propose a pipeline that aligns large language models to manage ambiguous queries . they propose to use their own assessment of perceived ambiguity to detect and manage queries a . |
| Outcome: | Experimental results show that APA empowers LLMs to detect and manage ambiguous queries while retaining the ability to answer clear questions. |
Unsupervised Neural Machine Translation for Low-Resource Domains via Meta-Learning (2021.acl-long)
Copied to clipboard
| Challenge: | Unsupervised machine translation suffers from data-scarce domains, authors report . a meta-learning algorithm trains the model to adapt to another domain by utilizing only a small amount of training data. |
| Approach: | They propose a meta-learning algorithm that trains the model to adapt to another domain . their model surpasses a transfer learning-based approach by up to 2-3 BLEU scores . |
| Outcome: | The proposed algorithm outperforms a transfer learning-based approach by 2-3 BLEU scores . the proposed model outperformed previous models in the domain of unsupervised machine translation . |
KMMLU: Measuring Massive Multitask Language Understanding in Korean (2025.naacl-long)
Copied to clipboard
Guijin Son, Hanwool Lee, Sungdong Kim, Seungone Kim, Niklas Muennighoff, Taekyoon Choi, Cheonbok Park, Kang Min Yoo, Stella Biderman
| Challenge: | Recent models struggle to show performance over 60%, significantly below the pass mark of the source exams (80%), highlighting the room for improvement. |
| Approach: | They propose to use Korean exams to collect 35,030 questions from an expert-level multiple choice model to capture linguistic and cultural aspects of the Korean language. |
| Outcome: | The proposed benchmark is based on 35,030 questions from original Korean exams. |
Enhancing Hallucination Detection via Future Context (2026.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. |
| Approach: | They propose a framework for detection of hallucinations in black-box generators by analyzing future contexts. |
| Outcome: | The proposed framework improves on existing methods and demonstrates that it is feasible to integrate it with other models. |
Sommelier: Scalable Open Multi-turn Audio Pre-processing for Full-duplex Speech Language Models (2026.acl-industry)
Copied to clipboard
| Challenge: | Existing high-quality conversational data is limited for full-duplex models . overlapping and backchanneling are a challenge for most systems . |
| Approach: | They propose a robust and scalable open-source data processing pipeline for full-duplex models. |
| Outcome: | The proposed pipeline can listen and speak simultaneously, supporting more fluid and human-like interaction. |
PePe: Personalized Post-editing Model utilizing User-generated Post-edits (2023.findings-eacl)
Copied to clipboard
| Challenge: | Existing neural machine translation models ignore personal style in their translations, but in these studies the definition of personal style is over-simplified. |
| Approach: | They propose a personalized automatic post-editing framework that generates sentences considering distinct personal behaviors by collecting post-edited data from a live machine translation system and combining a discriminator module and user-specific parameters. |
| Outcome: | The proposed model outperforms baseline models on four different metrics including BLEU, TER, YiSi-1, and human evaluation. |
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation (2022.findings-acl)
Copied to clipboard
| Challenge: | Current research in NMT Domain Adaptation rarely provides insights on the amount of data required to perform Domain . |
| Approach: | They propose a Domain adaptation learning curve prediction model that predicts prospective DA performance based on in-domain monolingual samples in the source language. |
| Outcome: | The proposed model predicts prospective DA performance based on in-domain monolingual samples in the source language. |
Code-Switching Curriculum Learning for Multilingual Transfer in LLMs (2025.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit near human-level performance in various tasks, but performance drops after a handful of high-resource languages due to the imbalance in pre-training data. |
| Approach: | They propose a code-switching curriculum learning model to enhance cross-lingual transfer for LLMs by progressively training models with a curriculum consisting of token-level code-changing, sentence-level codeswitching, and monolingual corpora. |
| Outcome: | The proposed model improves language transfer to Korean, with significant gains in Japanese and Indonesian . the proposed model mitigates spurious correlations between language resources and safety alignment . |
Specializing Multi-domain NMT via Penalizing Low Mutual Information (2022.emnlp-main)
Copied to clipboard
| Challenge: | Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. |
| Approach: | They propose a method that penalizes low MI to be higher for domain-specific NMTs. |
| Outcome: | The proposed method achieves state-of-the-art performance among current models . it also promotes low MI to be higher resulting in domain-specialized multi-domain NMT. |