Papers by Dongkwan Kim
LLM-as-an-Interviewer: Beyond Static Testing Through Dynamic LLM Evaluation (2025.findings-acl)
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| Challenge: | Recent work on LLM-as-a-Judge has reported higher correlations with human judgments due to its static nature. |
| Approach: | They propose a framework that leverages multi-turn interactions where the LLM interviewer actively provides feedback on responses and poses follow-up questions to the evaluated LLM. |
| Outcome: | The proposed framework evaluates six models on reasoning, factuality and instruction-following tasks. |
Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models (2024.emnlp-main)
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| Challenge: | While theory of mind (ToM) is naturally developed for humans in childhood, large language models (LLMs) exhibit inconsistency in ToM tasks, despite early reports of successful cases. |
| Approach: | They propose to evaluate human ToM precursors-perception inference and perception-to-belief inference-in large language models (LLMs) by annotating characters’ perceptions on ToMi and FANToM. |
| Outcome: | The proposed method significantly improves LLMs’ performance in false belief scenarios. |
Additive Compositionality of Word Vectors (D19-55)
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| Challenge: | Existing research on justifying additive compositionality of word embedding models requires a rather strong assumption of uniform word distribution. |
| Approach: | They propose to relax the assumption of uniform word distribution and propose more realistic conditions for proving additive compositionality. |
| Outcome: | The proposed model improves on word similarity and noisy sentence similarity. |
MUG-Eval: A Proxy Evaluation Framework for Multilingual Generation Capabilities in Any Language (2025.findings-emnlp)
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| Challenge: | Evaluating text generation capabilities of large language models (LLMs) is challenging, especially for low-resource languages where methods for direct assessment are scarce. |
| Approach: | They propose a framework that transforms existing benchmarks into conversational tasks and measures LLMs’ accuracies on those tasks. |
| Outcome: | The proposed framework correlates strongly with established benchmarks while enabling standardized comparisons across languages and models. |