Papers by CheolWon Na
ReFEree: Reference-Free and Fine-Grained Method for Evaluating Factual Consistency in Real-World Code Summarization (2026.acl-long)
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| Challenge: | Existing methods for evaluating factual consistency are primarily designed for short summaries of isolated code snippets. |
| Approach: | They propose a reference-free and fine-grained method for evaluating factual consistency in real-world code summaries. |
| Outcome: | The proposed method achieves highest correlation with human judgment among 13 baselines, improving 15-18% over the previous state-of-the-art. |
EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL (2026.findings-acl)
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| Challenge: | Existing RL methods assign query-level rewards to all clauses, treating correct and incorrect clauses equally. |
| Approach: | They propose a method which provides fine-grained supervision through clause-level rewards. |
| Outcome: | Experiments on widely-used Text-to-SQL benchmarks show that EXPO-SqL outperforms existing methods by fine-grained clause-level learning. |
DIP: Dead code Insertion based Black-box Attack for Programming Language Model (2023.acl-long)
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| Challenge: | Existing methods to attack natural language models are difficult to apply due to the requirements. |
| Approach: | They propose a black-box attack method that generates adversarial examples using dead code insertion. |
| Outcome: | The proposed method outperforms the state-of-the-art black-box attack in both attack efficiency and attack quality on 9 victim downstream-task large code models. |
Learning Sequential and Structural Information for Source Code Summarization (2021.findings-acl)
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| Challenge: | Existing models that learn both sequential and structural features of source code are limited in their use. |
| Approach: | They propose a model that learns both the sequential and the structural features of code for source code summarization. |
| Outcome: | The proposed model outperforms the state-of-the-art for source code summarization by experiments and human evaluations. |
UCGRec: User-Centric Graph Learning for LLM-based Sequential Recommendation (2026.findings-acl)
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| Challenge: | Existing methods for sequential recommendation rely primarily on item descriptions or utilize user preferences independently. |
| Approach: | They propose a method that integrates diverse user-relevant preference signals into a unified user-centric graph and injects the graph-based knowledge into the LLM through end-to-end training with graph neural networks. |
| Outcome: | The proposed method outperforms conventional and state-of-the-art methods on four widely used sequential real-world recommendation datasets. |
CoRAC: Integrating Selective API Document Retrieval with Question Semantic Intent for Code Question Answering (2025.naacl-long)
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| Challenge: | Existing automated code question answering methods provide accurate and relevant answers to questions about code. |
| Approach: | They propose a knowledge-based framework that generates precise code question answers by analyzing code snippets. |
| Outcome: | The proposed framework generates high-quality answers compared to large language models, such as ChatGPT. |
Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation (2025.findings-naacl)
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| Challenge: | Existing adversarial approaches to verify the vulnerability of language models require numerous queries and the information on the target model. |
| Approach: | They propose a method that generates adversarial examples without accessing the target model and uses controlled generation techniques to generate adversarials. |
| Outcome: | The proposed method generates adversarial examples without accessing the target model and is highly transferable and high quality. |