Papers by Sohee Yoon
Exploring the Practicality of Generative Retrieval on Dynamic Corpora (2024.emnlp-main)
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| Challenge: | a lack of comprehensive comparison between GR and Dual Encoders in IR systems is limiting . GR is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, and efficient in terms of inference FLOPs (x2), indexing time (x6) and storage footprint (x4) |
| Approach: | They propose to use autoregressive language models to perform information retrieval (IR) their results highlight the potential of GR for future use in practical IR systems . |
| Outcome: | The proposed model is more adaptable to evolving knowledge (4–11%), robust in learning knowledge with temporal information, efficient inference FLOPs (x2), indexing time (x6), and storage footprint (x4) compared to the most common model, Dual Encoder (DE). |
Single Ground Truth Is Not Enough: Adding Flexibility to Aspect-Based Sentiment Analysis Evaluation (2025.naacl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a challenging task of extracting sentiments along with their corresponding aspects and opinion terms from text. |
| Approach: | They propose a pipeline that expands existing evaluation sets by adding alternative valid terms for aspect and opinion. |
| Outcome: | The proposed evaluation set uncovers the capabilities of large language models (LLMs) in ABSA tasks, which is concealed by the single-answer GT sets. |
TelBench: A Benchmark for Evaluating Telco-Specific Large Language Models (2024.emnlp-industry)
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Sunwoo Lee, Dhammiko Arya, Seung-Mo Cho, Gyoung-eun Han, Seokyoung Hong, Wonbeom Jang, Seojin Lee, Sohee Park, Sereimony Sek, Injee Song, Sungbin Yoon, Eric Davis
| Challenge: | a growing demand for Large Language Models (LLMs) is requiring specialized models to augment customer service agents' skills. |
| Approach: | They propose a methodology for developing a specialized Telecommunications LLM . they use a dataset to evaluate customer service expertise in the telecommunications domain . |
| Outcome: | The proposed model improves the efficiency of customer service agents and reduces response times. |
TelAgentBench: A Multi-faceted Benchmark for Evaluating LLM-based Agents in Telecommunications (2025.emnlp-industry)
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Sunwoo Lee, Daseong Jang, Dhammiko Arya, Gyoung-eun Han, Injee Song, SaeRom Kim, Sangjin Kim, Seojin Lee, Seokyoung Hong, Sereimony Sek, Seung-Mo Cho, Sohee Park, Sungbin Yoon, Wonbeom Jang, Eric Davis
| Challenge: | Large Language Models (LLMs) are becoming powerful agentic systems . generic benchmarks fail to assess realistic, non-English performance . |
| Approach: | They propose to evaluate five core agentic capabilities: Reasoning, Planning, Action (tool-use), Retrieval-Augmented Generation, and Instruction Following. |
| Outcome: | The evaluations reveal significant performance disparities between models that employ explicit reasoning and those that do not. |
Knowledge Unlearning for Mitigating Privacy Risks in Language Models (2023.acl-long)
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| Challenge: | Recent work shows that an adversary can extract training data from Pretrained Language Models including Personally Identifiable Information (PII) such as names, phone numbers, and email addresses. |
| Approach: | They propose to use knowledge unlearning to reduce privacy risks for LMs by performing gradient ascent on target token sequences instead of trying to unlearn all the data at once. |
| Outcome: | The proposed method can give a stronger empirical privacy guarantee in scenarios where the data vulnerable to extraction attacks are known a priori while being much more efficient and robust. |