Papers by Dongsuk Oh
Don’t Judge a Language Model by Its Last Layer: Contrastive Learning with Layer-Wise Attention Pooling (2022.coling-1)
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| Challenge: | Recent pre-trained language models (PLMs) have shown competitive performance on many natural language processing tasks. |
| Approach: | They propose a pooling strategy which preserves layer-wise signals captured in each layer and learns digested linguistic features for downstream tasks. |
| Outcome: | The proposed method improves on standard semantic textual similarity and semantic search tasks. |
I Know What You Asked: Graph Path Learning using AMR for Commonsense Reasoning (2020.coling-main)
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| Challenge: | a large amount of pre-defined commonsense knowledge is available for commonsensense reasoning . humans acquire commonsence in their lives, but machines cannot learn commonseense without assistance. |
| Approach: | They propose an AMR-ConceptNet-Pruned (ACP) graph that is pruned from a full integrated graph . they show that the ACP graph interprets the reasoning path and predicts the correct answer . |
| Outcome: | The proposed graph outperforms baseline models in the commonsenseQA task . it shows that the reasoning path can be interpreted with the relations and concepts provided by the graph . |
CLEAR: Cross-Lingual Enhancement in Retrieval via Reverse-training (2026.acl-long)
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| Challenge: | Existing multilingual embedding models often struggle to capture cross-lingual alignment during training. |
| Approach: | They propose a novel loss function that leverages an English passage as a bridge to strengthen alignments between target language and English. |
| Outcome: | The proposed model improves retrieval performance across cross-lingual scenarios while minimizing performance degradation in English. |
Synthetic Paths to Integral Truth: Mitigating Hallucinations Caused by Confirmation Bias with Synthetic Data (2025.coling-main)
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| Challenge: | Existing methods to mitigate confirmation bias-induced hallucination in large language models (LLMs) however, they still exhibit issues such as confirmation bias, which remains unexplored in current research. |
| Approach: | They propose a method to mitigate confirmation bias-induced hallucination in large language models by using a synthetic data construction pipeline and direct preference optimization (DPO) training. |
| Outcome: | The proposed method improves response accuracy and reduced hallucination on natural questions open and halubench. |