Papers by Dongyub Lee
Ask, Assess, and Refine: Rectifying Factual Consistency and Hallucination in LLMs with Metric-Guided Feedback Learning (2024.eacl-long)
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| Challenge: | Recent advances in Large Language Models (LLMs) have heralded unprecedented capabilities in information seeking and text generation, but challenges remain regarding citation errors and generating information not present in the evidence (hallucination). |
| Approach: | They propose a framework to assess citation errors and hallucination using an explicit evaluation paradigm to formulate actionable natural language feedback. |
| Outcome: | The proposed approach improves correctness, fluency, and citation quality and reduces hallucinations in the results. |
You Truly Understand What I Need : Intellectual and Friendly Dialog Agents grounding Persona and Knowledge (2022.findings-emnlp)
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Jungwoo Lim, Myunghoon Kang, Yuna Hur, Seung Won Jeong, Jinsung Kim, Yoonna Jang, Dongyub Lee, Hyesung Ji, DongHoon Shin, Seungryong Kim, Heuiseok Lim
| Challenge: | Existing models that ground knowledge and persona at the same time are limited, leading to hallucination and a passive way of using personas. |
| Approach: | They propose a conversational agent that grounds external knowledge and persona simultaneously and a retrieval augmented generation model that generates utterances with lesser hallucination and more engagingness. |
| Outcome: | The proposed agent generates the utterance with lesser hallucination and more engagingness utilizing retrieval augmented generation with knowledge-persona enhanced query. |
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) consider relationships implicitly among subtasks at the word level. |
| Approach: | They propose a deep contextualized relation-aware network that allows interactive relations among subtasks . they propose self-supervised strategies that deal with multiple aspects . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three widely used benchmarks. |
Character-Level Feature Extraction with Densely Connected Networks (C18-1)
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| Challenge: | Existing methods to generate character-level features with neural architectures such as CNN or Recurrent Neural Network (RNN) are slow and generate position-independent features. |
| Approach: | They propose a method that uses a densely connected network to extract character-level features from words using CNN and RNN. |
| Outcome: | The proposed method shows robustness and effectiveness while being faster than CNN- or RNN-based methods. |
Reference and Document Aware Semantic Evaluation Methods for Korean Language Summarization (2020.coling-main)
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Dongyub Lee, Myeong Cheol Shin, Taesun Whang, Seungwoo Cho, Byeongil Ko, Daniel Lee, EungGyun Kim, Jaechoon Jo
| Challenge: | Existing methods for text summarization are based on recall-oriented understudy for gisting evaluation (ROUGE) scores do not reflect semantic meaning correspondences between generated and reference summaries. |
| Approach: | They propose to use Korean as a summarization language to generate a shorter form of text from the source document preserving salient information. |
| Outcome: | The proposed evaluation metrics improve the correlation between the metrics and human judgment. |