Papers by Yejin Cho
Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents (2025.emnlp-main)
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| Challenge: | Current efforts to bridge the two modes of interaction are reactive, focusing on responding to user inputs rather than coordinating dialogue flows. |
| Approach: | They propose a dataset designed for transition-aware dialogue modeling that incorporates structurally diverse and integrated mode flows. |
| Outcome: | The proposed dataset outperforms baseline models in intent detection and mode transition handling. |
Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation (2025.acl-long)
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| Challenge: | Visual programming languages (VPLs) allow users to create programs through graphical interfaces, which results in easier accessibility and widespread usage in various domains. |
| Approach: | They propose to train VPLs from user instructions using large language models . they propose to use retrieval-augmented fine-tuning to leverage repetitive use of subroutines . |
| Outcome: | The proposed method outperforms prompting-based methods for LD generation accuracy even with smaller backbone models. |
Disentangling Structure and Style: Political Bias Detection in News by Inducing Document Hierarchy (2023.findings-emnlp)
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| Challenge: | a new method to detect political bias in news articles overcomes this domain dependency . partisan bias exists in various social issues, including the 2016 presidential election . |
| Approach: | They propose a multi-head hierarchical attention model that encodes the structure of long documents through a diverse ensemble of attention heads. |
| Outcome: | The proposed model outperforms existing methods for detecting political bias in news articles. |
Leveraging WordNet Paths for Neural Hypernym Prediction (2020.coling-main)
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| Challenge: | Existing work on lexical relations based on distributed representations has differed widely. |
| Approach: | They propose a model that generates taxonomy paths for hypernym prediction using WordNet sequences. |
| Outcome: | The hypo2path model outperforms the best model by 4.11 points in hit-at-one (H@1) The proposed model outpersforms previous models by a factor of 0.9. |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |