Doyoung Kim (김도영)

I am an MS student studying AI. I am a member of Language & Knowledge Lab at KAIST AI, advised by Minjoon Seo. Before studying AI, I completed my BS in Mathematics & Computer Science (double major) at KAIST.

Despite the massive corpus of data on which modern AI systems are trained, they still struggle with tasks that humans, even young children, can easily perform. I believe that by incorporating key aspects of human cognitive processes, we can create AI systems capable of robust decision-making. My research focuses on narrowing the gap between human and artificial intelligence in complex scenarios. Specifically, I aim to tackle two key challenges:

• Extrapolability: Humans effortlessly generalize knowledge from simple scenarios to navigate complex situations. How can we develop AI agents that, after learning from a few simple demonstrations, can extrapolate to more complex scenarios?
• Semiparametric generation: Unlike purely parametric systems, humans rely on both learned patterns and direct interactions with memory, tools, and the physical world. Can we design AI systems that similarly combine internal models with external information sources in a cohesive semiparametric framework?

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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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