Papers by Sangyeop Kim
EXPERT: An Explainable Image Captioning Evaluation Metric with Structured Explanations (2025.findings-acl)
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| Challenge: | Existing studies on explainable evaluation metrics generate explanations without standardized criteria and the overall quality of the generated explanations remains unverified. |
| Approach: | They propose a reference-free evaluation metric that provides structured explanations based on fluency, relevance, and descriptiveness. |
| Outcome: | The proposed evaluation template achieves state-of-the-art on benchmark datasets while providing significantly higher-quality explanations than existing metrics. |
An Empirical Study of Group Conformity in Multi-Agent Systems (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. |
| Approach: | They analyze how LLM agents shape public opinion through debates on five contentious topics by simulating over 2,500 debates. |
| Outcome: | The proposed models show that LLM agents adopt specific stances over time and align with numerically dominant groups or more intelligent agents, exerting a greater influence. |
Finding Diamonds in Conversation Haystacks: A Benchmark for Conversational Data Retrieval (2025.emnlp-industry)
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| Challenge: | Our work identifies unique challenges in conversational data retrieval . large language model-based systems operate through open-ended interactions without predefined specifications. |
| Approach: | They propose a benchmark to evaluate systems that retrieve conversation data for product insights. |
| Outcome: | The benchmark provides a reliable standard for measuring conversational data retrieval performance. |
LLM-guided Plan and Retrieval: A Strategic Alignment for Interpretable User Satisfaction Estimation in Dialogue (2025.naacl-long)
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| Challenge: | Existing methods for estimating user satisfaction with dialogue systems face challenges due to limited understanding of underlying reasons for user dissatisfaction and high costs of annotating user intentions. |
| Approach: | They propose an interpretable framework for effective user satisfaction prediction . they propose to align utterances with strategies and large language models to retrieve relevant features from utterations. |
| Outcome: | The proposed framework achieves state-of-the-art performance on three benchmarks for the USE task. |
What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs (2025.acl-long)
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| Challenge: | Recent advances in context length extension have improved LLMs' performance and safety, but they present critical safety challenges. |
| Approach: | They investigate long-context vulnerabilities in Large Language Models (LLMs) using many-shot jailbreaking to exploit context length extension. |
| Outcome: | The proposed attacks do not require carefully crafted harmful content. |
HEISIR: Hierarchical Expansion of Inverted Semantic Indexing for Training-free Retrieval of Conversational Data using LLMs (2025.findings-naacl)
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| Challenge: | Existing methods for retrieving dialogue data often face challenges in capturing semantic intent or require extensive labeling and fine-tuning. |
| Approach: | They propose a framework that enhances semantic understanding in conversational data retrieval by optimizing data ingestion. |
| Outcome: | The proposed framework outperforms fine-tuned models in retrieval and intent analysis. |
Pre-Storage Reasoning for Episodic Memory: Shifting Inference Burden to Memory for Personalized Dialogue (2025.findings-emnlp)
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| Challenge: | Existing models place excessive reasoning burden on response generation, causing performance degradation . we present a new approach that shifts complex reasoning processes from inference to memory construction . |
| Approach: | a new approach shifts complex reasoning processes from inference to memory construction . PREMem extracts fine-grained memory fragments and establishes explicit relationships . small models achieve comparable results even with limited token budgets . |
| Outcome: | The proposed approach reduces the computational burden of response generation by capturing evolution patterns in memory items. |