SeungYoon Han


2024

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Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling
Huije Lee | Hoyun Song | Jisu Shin | Sukmin Cho | SeungYoon Han | Jong C. Park
Findings of the Association for Computational Linguistics: EMNLP 2024

Trolling in online communities typically involves disruptive behaviors such as provoking anger and manipulating discussions, leading to a polarized atmosphere and emotional distress. Robust moderation is essential for mitigating these negative impacts and maintaining a healthy and constructive community atmosphere. However, effectively addressing trolls is difficult because their behaviors vary widely and require different response strategies (RSs) to counter them. This diversity makes it challenging to choose an appropriate RS for each specific situation.To address this challenge, our research investigates whether humans have preferred strategies tailored to different types of trolling behaviors.Our findings reveal a correlation between the types of trolling encountered and the preferred RS. In this paper, we introduce a methodology for generating counter-responses to trolls by recommending appropriate RSs, supported by a dataset aligning these strategies with human preferences across various troll contexts. The experimental results demonstrate that our proposed approach guides constructive discussion and reduces the negative effects of trolls, thereby enhancing the online community environment.

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DSLR: Document Refinement with Sentence-Level Re-ranking and Reconstruction to Enhance Retrieval-Augmented Generation
Taeho Hwang | Soyeong Jeong | Sukmin Cho | SeungYoon Han | Jong Park
Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP

Recent advancements in Large Language Models (LLMs) have significantly improved their performance across various Natural Language Processing (NLP) tasks.However, LLMs still struggle with generating non-factual responses due to limitations in their parametric memory.Retrieval-Augmented Generation (RAG) systems address this issue by incorporating external knowledge with a retrieval module.Despite their successes, however, current RAG systems face challenges with retrieval failures and the limited ability of LLMs to filter out irrelevant information.Therefore, in this work, we propose DSLR (Document Refinement with Sentence-Level Re-ranking and Reconstruction), an unsupervised framework that decomposes retrieved documents into sentences, filters out irrelevant sentences, and reconstructs them again into coherent passages.We experimentally validate DSLR on multiple open-domain QA datasets and the results demonstrate that DSLR significantly enhances the RAG performance over conventional fixed-size passage.Furthermore, our DSLR enhances performance in specific, yet realistic scenarios without the need for additional training, providing an effective and efficient solution for refining retrieved documents in RAG systems.