Papers by Joosung Lee
PK-ICR: Persona-Knowledge Interactive Multi-Context Retrieval for Grounded Dialogue (2023.emnlp-main)
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| Challenge: | Identifying relevant persona or knowledge for conversational systems is difficult, but recent work has shown that it is more realistic to optimize for concrete persona. |
| Approach: | They propose a persona-knowledge dual context retrieval method that utilizes all dialogue contexts simultaneously. |
| Outcome: | The proposed method performs zero-shot top-1 knowledge retrieval and precise persona scoring. |
Enhancing Hallucination Detection via Future Context (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are widely used to generate plausible text on online platforms, without revealing the generation process. |
| Approach: | They propose a framework for detection of hallucinations in black-box generators by analyzing future contexts. |
| Outcome: | The proposed framework improves on existing methods and demonstrates that it is feasible to integrate it with other models. |
P5: Plug-and-Play Persona Prompting for Personalized Response Selection (2023.emnlp-main)
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| Challenge: | a plug-and-play persona prompting system can be used to generate personalized responses for real applications . a recent study shows that dialog context alone is insufficient for personalized response selection . |
| Approach: | They propose a plug-and-play persona prompting method that can be used in real applications . they show that the method performs well in the zero-shot setting . |
| Outcome: | The proposed method performs well in the zero-shot setting, and can be fine-tuned for even better performance. |
Enhanced Facet Generation with LLM Editing (2024.lrec-main)
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| Challenge: | Existing studies have shown that search engines can recognize facets of a user's query. |
| Approach: | They propose to use large language models to enhance the facets of a query to generate facets from a search engine. |
| Outcome: | The proposed model can predict facets by taking only queries as input without a search engine. |
CoMPM: Context Modeling with Speaker’s Pre-trained Memory Tracking for Emotion Recognition in Conversation (2022.naacl-main)
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| Challenge: | Emotion recognition in conversation is inaccurate if the previous utterances are not taken into account, so many studies reflect the dialogue context to improve the performance. |
| Approach: | They propose a method that combines pre-trained memory with the context model to improve the performance of the context models. |
| Outcome: | The proposed method achieves the first or second performance on all data and is state-of-the-art among systems that do not leverage structured data. |