Papers by Seunghan Yang
CIFLEX: Contextual Instruction Flow for Sub-task Execution in Multi-Turn Interactions with a Single On-Device LLM (2025.emnlp-main)
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| Challenge: | Experiments show that CIFLEX significantly reduces computational costs without degrading task performance. |
| Approach: | They propose a new execution system for efficient sub-task handling with a single large language model. |
| Outcome: | Experiments show that CIFLEX significantly reduces computational costs without degrading task performance. |
Chain-of-Rank: Enhancing Large Language Models for Domain-Specific RAG in Edge Device (2025.findings-naacl)
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| Challenge: | Retrieval-augmented generation (RAG) is valuable in specialized domains where precision is critical. |
| Approach: | They propose a chain-of-rank algorithm which allows LLMs to access a target domain early via finetuning. |
| Outcome: | The proposed method achieves state-of-the-art in benchmarks and analyzes its efficacy. |
Feedback Adaptation for Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing evaluation protocols focus on overall accuracy and fail to capture how systems adapt after feedback is introduced. |
| Approach: | They propose to use feedback adaptation as a problem setting for RAG systems . they propose a minimal inference-time instantiation that incorporates feedback without retraining . |
| Outcome: | The proposed evaluations show that training-based approaches exhibit a trade-off between delayed correction and reliable adaptation. |
Learning Contextual Retrieval for Robust Conversational Search (2025.emnlp-main)
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| Challenge: | Effective conversational search requires a deep understanding of user intent across multiple dialogue turns. |
| Approach: | They propose a novel LLM-based retriever that directly incorporates conversational context into the retrieval process. |
| Outcome: | The proposed method outperforms existing methods while incurring no additional inference overhead. |
Crayon: Customized On-Device LLM via Instant Adapter Blending and Edge-Server Hybrid Inference (2024.acl-long)
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| Challenge: | Large language models are highly advanced to user-requested tasks. |
| Approach: | They propose a device-server hybrid inference strategy for on-device LLM customization . they construct a pool of diverse base adapters and then blend them into a customized adapter . |
| Outcome: | The proposed method can be used on a large scale without extra training . it can be applied to large-scale LLMs without sacrificing the benefits of on-device customization. |