Papers by Taewon Yun
Learning to Verify Summary Facts with Fine-Grained LLM Feedback (2025.coling-main)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced the text summarization performance, but hallucination issues still occur in summaries. |
| Approach: | They propose a large-scale dataset containing fine-grained factual feedback on summaries that can be fine tuned by using Large Language Models (LLMs) they employ 10 distinct LLMs for diverse summary generation and Llama-3-70B-Instruct for feedback. |
| Outcome: | The proposed model outperforms models trained on smaller human-annotated datasets while maintaining high performance. |
UniSumEval: Towards Unified, Fine-grained, Multi-dimensional Summarization Evaluation for LLMs (2024.findings-emnlp)
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| Challenge: | Existing benchmarks for summarization quality evaluation lack diverse input scenarios, focus on narrowly defined dimensions, and struggle with subjective and coarse-grained annotation schemes. |
| Approach: | They propose to use AI to help human annotations and identifie potentially hallucinogenic input texts. |
| Outcome: | The proposed benchmarks improve on existing benchmarks in terms of input diversity, granularity of human annotations, and evaluation dimensions. |
Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts (2025.emnlp-main)
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Yuho Lee, Jiaqi Deng, Nicole Hee-Yeon Kim, Hyangsuk Min, Taewon Yun, Minjeong Ban, Kim Yul, Hwanjun Song
| Challenge: | HAMLET is a framework for evaluating the long-context comprehension of large language models. |
| Approach: | They propose a framework for evaluating the long-context comprehension of large language models . HAMLET structures key information of source texts into a three-level hierarchy . |
| Outcome: | HAMLET achieves 90% agreement with expert judgments while reducing evaluation cost by up to 25. |
Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding (2026.findings-acl)
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| Challenge: | Existing curation-based approaches to inference are inefficient and fail to adapt dynamically, leading to redundant sampling and missed opportunities for complementary reasoning. |
| Approach: | They propose a collaborative multi-teacher decoding framework that performs step-wise reasoning synthesis guided by predictive perplexity–based scoring and beam search. |
| Outcome: | The proposed framework generates higher-quality reasoning data and achieves student-level results, demonstrating that fine-grained collaboration yields structured, efficient, and robust reasoning distillation. |
Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages (2025.acl-long)
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Hyangsuk Min, Yuho Lee, Minjeong Ban, Jiaqi Deng, Nicole Hee-Yeon Kim, Taewon Yun, Hang Su, Jason Cai, Hwanjun Song
| Challenge: | Existing evaluation frameworks for text summarization lack domain-specific assessment criteria and are predominantly English-centric. |
| Approach: | They propose a multi-dimensional, multi-domain evaluation of summarization in English and Chinese that incorporates specialized assessment criteria for each domain and leverages a debate system to enhance annotation quality. |
| Outcome: | The proposed evaluation framework provides a multi-dimensional, multi-domain evaluation of summarization in English and Chinese. |
Learning to Summarize from LLM-generated Feedback (2025.naacl-long)
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| Challenge: | Developing effective text summarizers remains a challenge due to issues like unfaithful statements, key information omissions, and verbosity. |
| Approach: | They propose a large-scale dataset containing multi-dimensional feedback on LLM-generated summaries of varying quality across diverse domains to align them with human preferences for faithfulness, completeness, and conciseness. |
| Outcome: | The proposed model outperforms the 10x larger Llama3-70b-instruct in generating human-preferred summaries. |