Papers by Kyungho Kim
RT-VQ2A2: Real Time Vector Quantized Question Answering with ASR (2024.lrec-main)
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| Challenge: | Existing frameworks for QA with large language models are difficult to implement due to noise, limited context length and latency. |
| Approach: | They propose a model-agnostic framework to address problems in QA with large language models. |
| Outcome: | The proposed framework reduces noise in the ASR output and the limited context length of LLMs and improves performance on the widely used Spoken-SQuAD dataset. |
Query Generation for Multimodal Documents (2021.eacl-main)
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| Challenge: | Existing approaches to find relevance for multimodal documents with images are expensive and require a lot of runtime overhead. |
| Approach: | They propose to attach generated queries to doc-uments and index them to narrow down to candidate matches using inverted index. |
| Outcome: | The proposed model improves relevance ranking for multimodal documents with images . the proposed model can achieve the state of the art in the first stage retrieval scenarios . |
Anonpsy: A Graph-Based Framework for Structure-Preserving De-identification of Psychiatric Narratives (2026.findings-acl)
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| Challenge: | Existing approaches to de-identify narratives operate at the text level and offer limited control over which semantic elements are preserved or altered. |
| Approach: | They propose a de-identification framework that reformulates the task as graph-guided semantic rewriting. |
| Outcome: | The proposed framework preserves diagnostic fidelity while achieving low re-identification risk. |
Pre-Deployment Advertisement Ranking under Data Scarcity via Context-Aware Criteria Generation with VLMs (2026.acl-industry)
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Kyungho Kim, Yeonje Choi, Gyurim Hwang, Sejin Chung, Hongseok Lee, Myeong Ho Song, Yeongho Kim, Sunwoo Kim, Jongha Lee, Juyeon Kim, Kijung Shin
| Challenge: | Existing VLMs perform well on general multimodal tasks, but limited labeled data makes them difficult to apply to real-world business decisions. |
| Approach: | They propose a new task that aims to rank ads for a target brand prior to deployment . they propose 'brand-specific ad ranking' which uses brand-specific effectiveness . |
| Outcome: | The proposed task outperforms baselines on 10 brands on real-world advertising data. |
Cross-task Knowledge Transfer for Extremely Weakly Supervised Text Classification (2023.findings-acl)
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| Challenge: | Existing methods for text classification with extremely weak supervision impose stricter supervision constraints than those under regular weak supervision. |
| Approach: | They propose a framework that creates weak labels by leveraging recent developments in zero-shot text classification. |
| Outcome: | The proposed framework outperforms existing methods on weak labels generated by weakly supervise classification. |
Non-Essential Is NEcessary: Order-agnostic Multi-hop Question Generation (2024.lrec-main)
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| Challenge: | Existing multi-hop question generation methods treat answer-irrelevant documents as non-essential and remove them as impurities, which can lead to a decrease in model performance. |
| Approach: | They propose a task which leverages non-essential data in the training phase to create a robust model and extract the consistent embeddings in real-world inference environments. |
| Outcome: | The proposed model can perform ranker and generator without external modules and achieves state-of-the-art on a hotpotQA dataset. |