Papers by Seokju Hwang
Data-Efficient Adaptation to Contextual Shifts in LLM-based Conversational Recommendation (2026.findings-acl)
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| Challenge: | Existing data selection methods struggle to distinguish learnable samples under contextual shifts. |
| Approach: | They propose a framework agnostic to underlying large language model-based conversational recommender systems (CRSs) that captures user preferences through free-form conversations and generates contextually relevant recommendations. |
| Outcome: | The proposed framework outperforms baselines on three CRS benchmarks with real-world temporal splits. |
Which bird does not have wings: Negative-constrained KGQA with Schema-guided Semantic Matching and Self-directed Refinement (2026.findings-acl)
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| Challenge: | Existing KGQA benchmarks and methods are biased toward positive and calculation constraints. Negative constraints are neglected, although they frequently appear in real-world questions. |
| Approach: | They propose a task where each question contains at least one negative constraint and a corresponding dataset, NestKGQA. |
| Outcome: | The proposed framework outperforms baselines on both KGQA and NEST-KGQA benchmarks under few-shot settings. |
LLMs as Knowledge Graph Refiners: Mitigating Factual Inconsistencies in Generative Knowledge Extraction (2026.acl-long)
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| Challenge: | Knowledge graphs (KGs) represent real-world entities and their relations in a structured form. |
| Approach: | They propose a framework that performs triple-level refinement on KGs constructed via GKE. |
| Outcome: | The proposed framework improves KG quality from diverse perspectives. |