Papers by Jungsoo Park
FaVIQ: FAct Verification from Information-seeking Questions (2022.acl-long)
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| Challenge: | Existing fact verification datasets with crowdsourced claims introduce subtle biases that are difficult to control for. |
| Approach: | They construct a large-scale fact verification dataset with ambiguous questions . they use a corpus of 188k claims to construct false and true claims . |
| Outcome: | The proposed dataset outperforms models trained on the dataset FEVER or in-domain data by up to 17% absolute. |
Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs (2025.acl-long)
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| Challenge: | Recent surveys of literature highlight the overwhelming growth of Large Language Models (LLMs). |
| Approach: | They propose a semi-automated literature analysis approach that automates literature analysis using LLMs. |
| Outcome: | The proposed approach reduces paper surveying and data extraction by 93% compared to manual methods. |
Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering (2021.acl-long)
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| Challenge: | Existing approaches do not explicitly train QA models on how to resolve conversational dependency, and thus these models are limited in understanding human dialogues. |
| Approach: | They propose a framework that generates self-contained questions that can be understood without the conversation history and then trains a QA model with the pairs of original and self-constructed questions using a consistency-based regularizer. |
| Outcome: | The proposed framework improves the models’ performance by up to 1.2 F1 on QuAC, and 5.2 F1 for CANARD, while addressing the limitations of the existing approaches. |
Consistency Training with Virtual Adversarial Discrete Perturbation (2022.naacl-main)
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| Challenge: | Existing methods for regularizing a model are agnostic to the training model and may not be effective for perturbed inputs. |
| Approach: | They propose an augmentation method of adding a discrete noise that would incur the highest divergence between predictions by replacing tokens while keeping original semantics. |
| Outcome: | The proposed method outperforms baselines on semi-supervised text classification tasks and a robustness benchmark. |
Adversarial Subword Regularization for Robust Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Existing methods for segmenting words into subword units are not robust enough to handle multiple subword candidates. |
| Approach: | They propose to regularize subword segmentations that maximize the translation loss by using gradient signals during training to prevent erroneous segmentations of unseen words. |
| Outcome: | The proposed method improves the performance of NMT models on low-resource and out-domain datasets. |
Optimizing Test-Time Query Representations for Dense Retrieval (2023.findings-acl)
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| Challenge: | Recent developments of dense retrieval rely on quality representations of queries and contexts from pre-trained query and context encoders. |
| Approach: | They propose a test-time optimization of query representations that provides fine-grained pseudo labels over retrieval results. |
| Outcome: | The proposed algorithm improves open-domain question answering accuracy and direct re-ranking by up to 2.0% while running 1.3–2.4x faster with an efficient implementation. |