Papers by Yongrae Jo
Shifting from Ranking to Set Selection for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Existing approaches primarily rerank top-k passages based on individual relevance, often failing to meet the information needs of complex queries in multi-hop question answering. |
| Approach: | They propose a set-wise passage selection approach and introduce SetR which explicitly identifies the information requirements of a query through Chain-of-Thought reasoning. |
| Outcome: | The proposed approach outperforms both proprietary LLM-based rerankers and open-source baselines in terms of answer correctness and retrieval quality. |
Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt (2023.findings-emnlp)
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| Challenge: | Recent studies show that adding a instruction tuning stage to training large language models can improve zero-shot task generalization. |
| Approach: | They propose a method that retrieves promptspecific source prompt embeddings from training instances . they train soft prompt embeds for each prompt through prompt tuning and store the samples . |
| Outcome: | The proposed method outperforms hard prompts on unseen tasks by 2.39% points and outperformed 10 out of 11 datasets. |
Fixed Input Parameterization for Efficient Prompting (2023.findings-acl)
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| Challenge: | Recent studies have shown that attaching prompts to the input is effective at conditioning Language Models (LMs) however, prompts are always included in the input text during inference, thus incurring substantial computational and memory overhead. |
| Approach: | They propose to inject a fixed prompt into the parameters of an LM to be an efficient alternative to attaching fixed prompts to the input. |
| Outcome: | The proposed method can be up to 280 times more efficient in terms of FLOPs than previous approaches. |
Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (2024.naacl-long)
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| Challenge: | Recent studies have conjectured that multimodal hallucination is due to the vision encoder failing to ground on the image properly. |
| Approach: | They propose a multimodal self-feedback guided revision model that leverages visual cues to generate feedback to its initial response based on the visual information provided by the vision encoder. |
| Outcome: | The proposed model reduces multimodal hallucination and outperforms previous models on MMHal-Bench, POPE, and GAVIE. |