Papers by Jaehee Kim
Read the Room, Read the Image: Understanding Indirect Speech Acts in Multimodal Visual Contexts (2026.findings-acl)
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Jaehee Kim, Ji Hoon Chung, Seoyoon Park, Unsol Kim, Kyungwon Park, JiHak Kim, Yi-Jun Chen, Hansaem Kim
| Challenge: | Existing benchmarks focus on explicit context, but do not address context-dependent pragmatic understanding. |
| Approach: | They propose a benchmark for evaluating ISA understanding through integrated reasoning over visual context and dialogue. |
| Outcome: | Experiments show that state-of-the-art models struggle with visually grounded indirect speech acts . linguistic meaning emerges through the relationship between an utterance and situational context . |
Verbosity-Aware Rationale Reduction: Sentence-Level Rationale Reduction for Efficient and Effective Reasoning (2025.findings-acl)
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| Challenge: | Recent advances in Large Language Models (LLMs) demonstrate remarkable reasoning capabilities comparable to human cognitive abilities. |
| Approach: | They propose a sentence-level rationale reduction framework that leverages likelihood-based criteria to selectively remove redundant reasoning sentences while preserving reasoning capabilities. |
| Outcome: | The proposed framework improves performance by 7.71% while reducing token generation by 19.87% compared to models trained with complete reasoning paths. |
CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists (2025.emnlp-main)
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| Challenge: | Existing evaluation protocols for text generation suffer from rating inconsistencies . lexical overlap-based metrics align poorly with human judgments . |
| Approach: | They propose a checklist-based evaluation framework that improves rating reliability via decomposed binary questions. |
| Outcome: | The proposed framework improves rating reliability by decomposing binary questions . it improves agreement across evaluator models by 0.45 and reduces score variance . human evaluation remains the gold standard, but it #, Equal contribution. |
Boosting Prompt-Based Self-Training With Mapping-Free Automatic Verbalizer for Multi-Class Classification (2023.findings-emnlp)
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| Challenge: | Recent prompt-based fine-tuning techniques have garnered considerable interest as a core technique for few-shot text classification tasks. |
| Approach: | They propose a prompt-based fine-tuning approach that reformulates the fine-uning objective to align with the Masked Language Modeling objective. |
| Outcome: | The proposed method has shown superior performance on five multi-class classification datasets. |