Papers by Sukmin Cho
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) enable zero-shot approaches in open domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. |
| Approach: | They propose to use a distraction-aware answer selection framework to mitigate the impact of irrelevant documents in the retrieved set and the overconfidence of the generated answers to enhance the performance of zero-shot readers. |
| Outcome: | The proposed approach handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. |
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation (2025.findings-acl)
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| Challenge: | incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. large language models are effective for generating explanatory rationales, but inconsistencies in relevance and domain alignment of LLM-generated rationale are challenging. |
| Approach: | They propose a framework that fine-tunes smaller language models with rationales exhibiting high domain relevance . they propose 'quality-focused' approach that selects rationale based on their alignment with clinical reasoning . |
| Outcome: | The proposed framework improves mental health detection and interpretation performance by ensuring high-quality rationales with domain relevance. |
The RAG Paradox: A Black-Box Attack Exploiting Unintentional Vulnerabilities in Retrieval-Augmented Generation Systems (2025.findings-emnlp)
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| Challenge: | Existing approaches to degrade retrieval-augmented generation systems rely on unrealistic assumptions in which external attackers have access to internal components such as the retriever. |
| Approach: | They propose a black-box attack based on the RAG paradox, a structural vulnerability that emerges from the system’s effort to enhance trust by revealing both the retrieved documents and their sources to users. |
| Outcome: | The proposed method significantly degrades system performance without internal access while generating natural-looking poisoned documents. |
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025.findings-naacl)
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Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C. Park, Youngjin Kwon
| Challenge: | Existing methods for drafting and verifying tokens require significant fine-tuning or have inconsistent performance across tasks. |
| Approach: | They propose a lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality. |
| Outcome: | The proposed method outperforms existing database drafting methods on Spec-Bench using 7B and 13B parameters. |
Database-Augmented Query Representation for Information Retrieval (2025.emnlp-main)
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| Challenge: | Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, but the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. |
| Approach: | They propose a database-augmented Query representation framework which augments the query with various (query-related) metadata across multiple tables. |
| Outcome: | The proposed framework significantly improves overall retrieval performance over baselines. |
Typos that Broke the RAG’s Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations (2024.findings-emnlp)
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| Challenge: | Existing studies on the robustness of Large Language Models (LLMs) overlook the interconnected relationships between RAG components or the potential threats prevalent in real-world databases, such as minor textual errors. |
| Approach: | They propose a novel attack method that exploits vulnerabilities in RAG components and tests its robustness against noisy documents. |
| Outcome: | The proposed method devastates the performance of each component and their synergy, and significantly devases the performance. |
Towards Effective Counter-Responses: Aligning Human Preferences with Strategies to Combat Online Trolling (2024.findings-emnlp)
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| Challenge: | Existing methods to counter trolling in online communities are not yet available to address the diversity of trolling behaviors. |
| Approach: | They propose a method for generating counter-responses to trolls by aligning these strategies with human preferences across different trolled contexts. |
| Outcome: | The proposed approach reduces negative effects of trolling and improves the online community environment. |
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Current retrieval-augmented generation systems struggle when retrieval models fail to rank the most relevant documents . existing extractive methods reduce latency but rely on independent, non-adaptive sentence selection . |
| Approach: | They introduce an extractive context compression framework that enhances retrieval-augmented generation in question answering. |
| Outcome: | EXIT surpasses existing compression methods and uncompressed baselines in QA accuracy . the framework reduces inference time and token count while preserving contextual dependencies . |
An Efficient Gloss-Free Sign Language Translation Using Spatial Configurations and Motion Dynamics with LLMs (2025.naacl-long)
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| Challenge: | Existing methods for sign language translation rely on glosses, which are written representations of signs. |
| Approach: | They propose a new LLM-based SLT framework that uses off-the-shelf visual encoders to extract spatial and motion features from sign videos. |
| Outcome: | The proposed framework captures spatial configurations and motion dynamics in sign language without domain-specific tuning. |
Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity (2024.naacl-long)
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| Challenge: | Recent Large Language Models (LLMs) generate factually incorrect answers based on their parametric memory. |
| Approach: | They propose a retrieval-augmented large language model that can dynamically select the most suitable strategy based on query complexity. |
| Outcome: | The proposed approach improves the performance of QA systems on open-domain QA datasets. |
Augmenting Document Representations for Dense Retrieval with Interpolation and Perturbation (2022.acl-short)
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| Challenge: | Existing sparse retrieval models rely on term-based matching to retrieve relevant documents. |
| Approach: | They propose a framework which augments the representations of documents with interpolation and perturbation. |
| Outcome: | The proposed framework significantly outperforms baselines on the dense retrieval of both the labeled and unlabeled documents. |
Semantic Hardness Is Not Visual Hardness: Sign-Aware Hard Negative Mining for Sign Language Retrieval (2026.acl-long)
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Junmyeong Lee, Chan Hur, ChangSu Choi, Sukmin Cho, Fitsum Gaim, Eui Jun Hwang, Hoyun Song, KyungTae Lim
| Challenge: | Existing methods for sign language retrieval fail to capture visual ambiguity . semantically distinct yet visually confusable signs are rarely treated as hard negatives . |
| Approach: | They propose a method that constructs hard negatives based on visual confusability rather than linguistic similarity. |
| Outcome: | The proposed method significantly improves fine-grained retrieval performance while preserving coarse-grain accuracy. |
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker (2023.findings-acl)
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| Challenge: | Existing studies suggest re-rankers by fine-tuning pre-trained language models . however, manual search for discrete prompts is expensive and sub-optimal in transferability . |
| Approach: | They propose a discrete prompt optimization method that guides the generated texts toward optimal prompts . they propose to use large-scale language models as a zero-shot re-ranker . |
| Outcome: | The proposed method improves the performance of the re-ranker against baselines and human prompts. |
Sign Language Production With Avatar Layering: A Critical Use Case over Rare Words (2022.lrec-1)
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| Challenge: | Existing vision-based sign language production approaches suffer from out-of-vocabulary (OOV) and test-time generalization problems. |
| Approach: | They propose an avatar-based sign language production system that generates sign language videos from spoken language expressions. |
| Outcome: | The proposed system achieves higher BLEU-4 and higher ROUGE-L scores on a new Korean-Korean sign language dataset. |
Test-Time Self-Adaptive Small Language Models for Question Answering (2023.findings-emnlp)
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| Challenge: | Recent instruction-finetuned large language models (LMs) have shown notable performances in various tasks, such as question-answering. |
| Approach: | They propose to use unlabeled test data to transfer smaller language models with limited knowledge. |
| Outcome: | The proposed strategy shows significant performance improvements on benchmark QA datasets with higher robustness across diverse prompts, enabling LMs to stay stable. |