Papers by Yu Jin Kim
Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning (2022.naacl-main)
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| Challenge: | Currently, commonsense reasoning systems are limited by expensive data annotations and overfitting to a specific benchmark. |
| Approach: | They propose to transform a commonsense knowledge graph into synthetic QA-form samples for model training. |
| Outcome: | The proposed framework improves performance with multiple commonsense KGs on five commonsensense reasoning benchmarks. |
Prospector: Improving LLM Agents with Self-Asking and Trajectory Ranking (2024.findings-emnlp)
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Byoungjip Kim, Youngsoo Jang, Lajanugen Logeswaran, Geon-Hyeong Kim, Yu Jin Kim, Honglak Lee, Moontae Lee
| Challenge: | Existing LLMs are limited in their ability to incorporate feedback from an environment. |
| Approach: | They propose an LLM agent that consists of an Actor and a Critic. |
| Outcome: | The proposed agent outperforms existing LLMs on benchmark environments and shows that it can generate diverse trajectories and pick the most rewarding trajectory. |
On Sample-Efficient Code Generation (2023.emnlp-industry)
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Hojae Han, Yu Jin Kim, Byoungjip Kim, Youngwon Lee, Kyungjae Lee, Kyungmin Lee, Moontae Lee, Kyunghoon Bae, Seung-won Hwang
| Challenge: | Existing approaches to code generation rely on rejection sampling to generate multiple code snippets then select the best. |
| Approach: | They propose a framework that prioritizes sampling on test problems that models can solve. |
| Outcome: | The proposed framework reduces sampling costs while maintaining comparable code generation performance. |
IRPO: Implicit Policy Regularized Preference Optimization (2026.findings-eacl)
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| Challenge: | Recent DPOs introduce additional hyperparameters, reducing feasibility for LLM fine-tuning. |
| Approach: | They propose an algorithm that regularizes the reward against a reference policy without extra hyperparameters to address suboptimal outcomes. |
| Outcome: | The proposed algorithm outperforms baseline algorithms with the same hyperparameter complexity while maintaining training simplicity. |
Overlapping Context with Variable-Length Stride Increases Diversity when Training Large Language Model for Code (2025.acl-industry)
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Geonmo Gu, Jaeho Kwak, Haksoo Moon, Hyun Seung Shim, Yu Jin Kim, Byoungjip Kim, Moontae Lee, Hyejeong Jeon
| Challenge: | Large language models for code (LLMs) are gaining more and more attention due to their wide applicability. |
| Approach: | They propose a method which extracts overlapping contexts from training data using variable-length stride. |
| Outcome: | The proposed method outperforms the conventional approach of controlling the number of epochs in terms of the pass@k rate. |
Adaptive Retrieval for Reasoning (2026.acl-long)
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| Challenge: | Existing reasoning-based rerankers suffer from bounded recall. |
| Approach: | They propose a framework that leverages adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive retrieval tasks by 5.6%pt. |
DuET: Dual Execution for Test Output Prediction with Generated Code and Pseudocode (2026.findings-acl)
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| Challenge: | Recent studies have shown that test output prediction is difficult to achieve due to code errors. |
| Approach: | They propose a framework that grounds prediction on error-resilient pseudocode and simulates execution via LLM reasoning to overcome limitations of direct execution suffering from code errors. |
| Outcome: | The proposed framework improves Pass@1 on LiveCodeBench, BigCodeBech-Hard, DevEval and HumanEval(+) and improves on pass@1 by 13.6 pp. |