Papers by SeongKu Kang
Pearl: A Review-driven Persona-Knowledge Grounded Conversational Recommendation Dataset (2024.findings-acl)
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Minjin Kim, Minju Kim, Hana Kim, Beong-woo Kwak, SeongKu Kang, Youngjae Yu, Jinyoung Yeo, Dongha Lee
| Challenge: | Existing datasets for conversational recommender systems lack specific user preferences and explanations for recommendations . current datasets lack specific preferences, hindering high-quality recommendations despite advances in large language models . |
| Approach: | They propose to synthesize a conversational recommendation dataset with persona- and knowledge-augmented LLM simulators to address these challenges. |
| Outcome: | The proposed dataset outperforms baselines in human and automatic evaluations. |
Taxonomy-guided Semantic Indexing for Academic Paper Search (2024.emnlp-main)
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| Challenge: | Academic paper search often struggles to match underlying academic concepts between queries and documents. |
| Approach: | They propose a framework that extracts key concepts from papers and organizes them as a semantic index guided by an academic taxonomy. |
| Outcome: | The proposed framework can be flexibly employed to enhance existing retrieval frameworks. |
Scientific Paper Retrieval with LLM-Guided Semantic-Based Ranking (2025.findings-emnlp)
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| Challenge: | Recent studies also use large language models (LLMs) for query understanding, but these methods lack grounding in corpus-specific knowledge and may generate unreliable or unfaithful content. |
| Approach: | They propose a paper retrieval framework that combines large language models (LLMs) with a concept-based semantic index to capture scientific concepts. |
| Outcome: | The proposed framework improves the performance of various base retrievers, surpasses strong existing LLM-based baselines, and remains highly efficient. |
Topic Coverage-based Demonstration Retrieval for In-Context Learning (2025.emnlp-main)
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| Challenge: | Prior methods to retrieve demonstrations based on embedding similarity or generation probability, resulting in irrelevant or redundant examples. |
| Approach: | They propose a topic coverage-based retrieval framework that selects demonstrations to comprehensively cover topic-level knowledge relevant to both the test input and the model. |
| Outcome: | The proposed framework covers all the necessary knowledge for the test input and the model. |
Self-Consistent Reasoning-based Aspect-Sentiment Quad Prediction with Extract-Then-Assign Strategy (2024.findings-acl)
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| Challenge: | Recent studies have developed powerful generative methods for aspect sentiment quad prediction (ASQP) but they still suffer from imprecise predictions and limited interpretability due to data scarcity and inadequate modeling of the quadruplet composition process. |
| Approach: | They propose a self-consistent reasoning-based aspect sentiment quadruple prediction framework which generates reasonings and corresponding quadruples in sequence. |
| Outcome: | The proposed model significantly improves its ability to handle complex reasoning tasks and correctly predict quadruplets through consistency voting, resulting in enhanced interpretability and accuracy in aspect sentiment quadr uplp prediction. |
Why These Documents? Explainable Generative Retrieval with Hierarchical Category Paths (2026.findings-acl)
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| Challenge: | Generative retrieval directly decodes a document identifier, making it impossible to provide explanations for its retrieval decision. |
| Approach: | They propose a hierarchical category path-Enhanced Generative Retrieval that generates category paths step-by-step and decodes docid. |
| Outcome: | The proposed method provides explanations for retrieval decision by generating hierarchical category paths step-by-step and decoding docid. |