Papers by Hanseok Oh
Generative Multi-hop Retrieval (2022.emnlp-main)
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| Challenge: | A bi-encoder approach to text retrieval has limitations in multi-hop settings; the reformulated query gets longer as the number of hops increases, which further tightens the embedding bottleneck of the query vector. |
| Approach: | They propose an encoder-decoder model that performs multi-hop retrieval by simply generating the entire text sequences of the retrieval targets. |
| Outcome: | The proposed model achieves comparable or higher performance than bi-encoder models in five datasets while demonstrating superior GPU memory and storage footprint. |
Subject-level Inference for Realistic Text Anonymization Evaluation (2026.acl-long)
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Myeong Seok Oh, Dong-Yun Kim, Hanseok Oh, Chaean Kang, Joeun Kang, Xiaonan Wang, Hyunjung Park, Young Cheol Jung, Hansaem Kim
| Challenge: | Existing text anonymization evaluations assume only a single data subject, ignoring multi-subject scenarios. |
| Approach: | They propose a benchmark that shifts the unit of evaluation from text spans to individuals . they show that subject-level inference protection drops as low as 33% when masked . |
| Outcome: | The proposed benchmark reduces the amount of protection available when PII spans are masked. |
Nonparametric Decoding for Generative Retrieval (2023.findings-acl)
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Hyunji Lee, JaeYoung Kim, Hoyeon Chang, Hanseok Oh, Sohee Yang, Vladimir Karpukhin, Yi Lu, Minjoon Seo
| Challenge: | Existing text retrieval models depend on the information encoded in its parameters without external memory, its information capacity is limited and fixed. |
| Approach: | They propose a nonparametric decoding approach which uses external memory instead of vanilla vocab embeddings as decoder voka embedds. |
| Outcome: | The proposed model can utilize parametric and nonparametric space. |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
KTRL+F: Knowledge-Augmented In-Document Search (2024.naacl-long)
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| Challenge: | KTRL+F is a knowledge-augmented in-document search that requires real-time identification of all semantic targets within a document with the awareness of external sources through a single natural query. |
| Approach: | They propose a knowledge-augmented in-document search that requires real-time identification of all semantic targets within a document with the awareness of external sources through a single natural query. |
| Outcome: | The proposed model reduces time for searching with less queries and reduced extra visits to other sources for collecting evidence. |
Instruction Tuning with and without Context: Behavioral Shifts and Downstream Impact (2026.eacl-long)
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Hyunji Lee, Seunghyun Yoon, Yunjae Won, Hanseok Oh, Geewook Kim, Trung Bui, Franck Dernoncourt, Elias Stengel-Eskin, Mohit Bansal, Minjoon Seo
| Challenge: | Prior work on instruction tuning datasets combined these data types without examining their distinct effects. |
| Approach: | They investigate how training LLMs with or without context affects model behavior and performance . they find that using context-augmented data as the backbone for vision-language models reduces hallucination . |
| Outcome: | The proposed training with context-augmented data reduces hallucination and improves grounding in the visual domain. |