Papers by Hojae Han
ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models (2024.acl-long)
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| Challenge: | Despite the critical role of software requirements, these criteria have not been studied actively in previous code generation works. |
| Approach: | They propose a framework that leverages in-context learning to organize and extrapolate unexpressed requirements from textual descriptions. |
| Outcome: | The proposed framework generates functional requirements from textual descriptions and extrapolates unexpressed requirements from them. |
ReACC: A Retrieval-Augmented Code Completion Framework (2022.acl-long)
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| Challenge: | Recent work has shown that statistical language modeling with transformers can greatly improve the performance in code completion tasks. |
| Approach: | They propose a retrieval-augmented code completion framework that combines a source code retriever and an auto-regressive language model for programming language. |
| Outcome: | The proposed framework achieves state-of-the-art on CodeXGLUE benchmark. |
R3-SQL: Ranking Reward and Resampling for Text-to-SQL (2026.findings-acl)
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| Challenge: | Existing rankers assign inconsistent scores to functionally equivalent SQL queries . ranking cannot recover when the correct SQL is absent from the pool. |
| Approach: | They propose a Text-to-SQL framework that rewards ranking and resampling . it first groups candidates by execution result and ranks groups for consistency . |
| Outcome: | The proposed framework achieves 75.03 execution accuracy on BIRD-dev, a new state of the art among methods using models with disclosed sizes. |
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering (D19-1)
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| Challenge: | Existing approaches for non-factoid question answering can be categorized into representation and interaction focused approaches. |
| Approach: | They propose a novel approach which derives contextualized uni-gram representation from n-grams. |
| Outcome: | The proposed approach achieves state-of-the-art in two public non-factoid question answering datasets. |
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. |
Intervention-Based Alignment of Code Search with Execution Feedback (2023.findings-emnlp)
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| Challenge: | Existing code search training datasets approximate text-code co-occurrences as positive execution feedback, but this approximation may misalign models’ retrieval decisions from ground-truth correctness. |
| Approach: | They propose a code intervention-based reinforcement learning approach that perturbs training code to result in misalignment, then tests models’ decisions and corrects them with the execution feedback by reinforcement learning. |
| Outcome: | The proposed method induces the execution feedback from perturbation, without actual execution, and then tests models’ decisions and corrects them with the execution input by reinforcement learning. |
Towards Compositional Generalization in Code Search (2022.emnlp-main)
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| Challenge: | Existing code search models that focus on code as an unstructured sequence fail to generalize when the lexical perturbation without changing structures and labels is applied in test codes. |
| Approach: | They propose a compositional generalization model that extracts structural elements and a code template that targets compositional genericization. |
| Outcome: | The proposed model is complementary to flow graphs in GraphCodeBERT, by enhancing structural context around variables. |
Learning with Limited Data for Multilingual Reading Comprehension (D19-1)
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| Challenge: | Existing approaches to support question answering in a new language with limited training resources introduce noises to the training data due to translation or generation errors. |
| Approach: | They propose a weakly-supervised framework that quantifies noises from automatically generated labels to deemphasize or fix noisy data in training. |
| Outcome: | The proposed framework can deemphasize or fix noisy data in training on low-resource languages with varying similarity to English. |
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. |
PERC: Plan-As-Query Example Retrieval for Underrepresented Code Generation (2025.coling-main)
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| Challenge: | Using large language models to generate code has shown significant promise, but selecting effective examples to improve generation quality remains a challenging task. |
| Approach: | They propose a framework that utilizes algorithmic plans to identify and retrieve effective examples. |
| Outcome: | The proposed framework outperforms the state-of-the-art RAG methods in code generation even when the source and target languages match or differ. |
PLEX: Adaptive Parameter-Efficient Fine-Tuning for Code LLMs using Lottery-Tickets (2025.naacl-industry)
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| Challenge: | PLEX is a lottery-ticket based parameter-efficient fine-tuning method that adapts large language models to well-supported and underrepresented programming languages (PLs) in pretraining. |
| Approach: | They propose a lottery-ticket based parameter-efficient fine-tuning method that adapts large language models to well-supported and underrepresented programming languages (PLs) |
| Outcome: | The proposed method achieves state-of-the-art performance among PEFT methods while maintaining competitive results with reduced computational overhead. |
Benchmarking Testing in Automated Theorem Proving (2026.acl-industry)
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| Challenge: | Existing evaluations rely on indirect proxies such as lexical overlap with human-annotated proof, or expensive manual inspection. |
| Approach: | They propose a framework that evaluates the semantic correctness of formal theorems . they use a set of problems paired with 41 successor theorels to compare them . |
| Outcome: | The proposed framework evaluates the semantic correctness of formal theorems using real-world Lean 4 repositories. |