Papers by Seongho Joe
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models (2024.eacl-long)
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| Challenge: | Abstractive summarization models generate factually inconsistent content when parametric knowledge conflicts with knowledge in the input document. |
| Approach: | They propose a method to enhance factual adaptiveness while achieving factual consistency on original datasets. |
| Outcome: | The proposed method improves factual adaptiveness while achieving factual consistency on original datasets. |
Model Intrinsic Features of Fine-tuning based Text Summarization Models for Factual Consistency (2023.findings-acl)
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| Challenge: | a summarization model with relatively low factual consistency is more likely to model summaries that are not conditional to the documents. |
| Approach: | They analyze the model intrinsic features by varying the fine-tuning objectives and datasets. |
| Outcome: | The proposed models have a high inductive bias for factual consistency and are more likely to model summaries that are not conditional to the documents. |
Correcting Negative Bias in Large Language Models through Negative Attention Score Alignment (2025.naacl-long)
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Sangwon Yu, Jongyoon Song, Bongkyu Hwang, Hoyoung Kang, Sooah Cho, Junhwa Choi, Seongho Joe, Taehee Lee, Youngjune Gwon, Sungroh Yoon
| Challenge: | Experimental results show that large language models exhibit a negative bias in binary decision tasks . hallucination is a factor that degrades reliability of LLMs . |
| Approach: | They propose a negative attention score to systematically and quantitatively formulate negative bias by using a parameter-efficient fine-tuning technique. |
| Outcome: | The proposed method reduces the gap between precision and recall caused by negative bias while preserving generalization abilities. |