Papers by Sohee Park
Spatial Dependency Parsing for Semi-Structured Document Information Extraction (2021.findings-acl)
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| Challenge: | Information extraction (IE) for semistructured document images is often considered as a sequence tagging problem . however, such a setup cannot handle complex spatial relationships and is not suitable for highly structured information. |
| Approach: | They propose a spatial dependency parsing problem that models complex spatial relationships . they evaluate it on receipts, name cards, forms, and invoices and compare it to other methods . |
| Outcome: | The proposed parser achieves similar or better performance on various kinds of documents compared to baselines including BERT-based IOB taggger. |
Unveiling the Response of Large Vision-Language Models to Visually Absent Tokens (2025.emnlp-main)
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| Challenge: | Large Vision-Language Models (LVLMs) generate contextually relevant responses by jointly interpreting visual and textual inputs. |
| Approach: | They propose a method to classify whether an input token is visually grounded by reinterpreting question prompts or replacing the detected absent tokens during generation. |
| Outcome: | The proposed method mitigates the models’ tendency to falsely presume the visual presence of text input and its generality across various LVLMs. |
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. |