Doyoung Kim (김도영)

My name is Doyoung Kim, and I am an incoming PhD student at NYU CS, advised by Prof. Sherry Yang.

My research interests is about general intelligence in langauge and robotics.

Currently I am a MS student at KAIST AI, advised by Prof. Minjoon Seo. Before studying AI, I completed my BS in Mathematics & Computer Science (double major) at KAIST.

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While humans can learn complex reasoning from few examples, AI struggles to generalize beyond its training. We enable language models to generate "cognitive maps" - tree-structured expansions of future states - before planning. In maze-solving tasks, this cognitive mapping approach proves to be the only effective method for helping language models extrapolate their planning abilities to larger, unseen mazes.

We propose a self-training method that helps LLMs identify their first incorrect reasoning step ("pit") and use it as a reward signal. Through preference optimization, this method enables LLMs to improve their reasoning process, leading to enhanced mathematical performance.

We introduce a semiparametric model superposing two embedding spaces: parametric token embeddings and nonparametric sequence embeddings. The model is co-trained using weighted cross-entropy loss for language modeling and InfoNCE loss for sequence retrieval to enable generation with citations.

We propose a fine-grained evaluation framework for generative language models based on 12 alignment skill sets, which show a strong correlation between model-based and human-based evaluations.

We introduce a new instruction-tuning dataset called the COT COLLECTION dataset, containing 1.84 million rationales across 1,060 tasks. These rationales were extracted from the FLAN Collection using OpenAI Codex with in-context learning (ICL). We fine-tune Flan-T5 (3B & 11B) with the COT COLLECTION to show both zero-shot and few-shot improvements.

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