Papers by Youngwon Lee
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. |
GRAD: Generalizing RAG Adaptation with Decoding (2026.acl-long)
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| Challenge: | Using GRAD, we can steer Retrieval-augmented generation objectives without retraining large language models. |
| Approach: | They propose an adaptive decoding-time framework that keeps the base generator fixed and composes small, objective-specific guidance at inference. |
| Outcome: | The proposed framework improves accuracy with favorable latency across public benchmarks and private settings with no in-domain labels while reliably activating helpful objectives and suppressing harmful ones, adaptively to tasks. |
Query Variant Detection Using Retriever as Environment (2025.naacl-industry)
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| Challenge: | Identifying query variants is nontrivial as highly similar query pairs may fail to differ in word form, order, or phrasing despite sharing the same intent. |
| Approach: | They propose to use retrieval as an environment feedback to capture semantic equivalence . experimental results demonstrate the efficacy of the proposed method . |
| Outcome: | The proposed method improves query variant detection across diverse scenarios. |
CORD: Balancing COnsistency and Rank Distillation for Robust Retrieval-Augmented Generation (2025.naacl-short)
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| Challenge: | Existing methods to ground large language models fail to adequately attend to all contexts . position bias is hindered by retrieval-augmented generation, which requires constant attention . |
| Approach: | They propose to augment and distill training instances with their perturbed positions to encourage consistent predictions . they also propose to balance COnsistency and Rank Distillation by combining noise-controlled perturbations with augmentation and distillation. |
| Outcome: | The proposed method outperforms existing methods in diverse RAG benchmarks. |
Learning to Rank Generation with Pairwise Partial Rewards (2023.emnlp-main)
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| Challenge: | Existing methods for conditional text generation suffer from large action space and delayed reward, as the reward can be computed only after an entire sequence is generated. |
| Approach: | They propose a method that provides partial rewards for intermediate actions taken on partial sequences to prioritize actions that lead to the generation of more desirable sequences. |
| Outcome: | The proposed method overcomes the limitations of the prevalent supervised maximum likelihood estimation approach. |
Agentic Verification for Ambiguous Query Disambiguation (2026.findings-acl)
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Youngwon Lee, Seung-won Hwang, Ruofan Wu, Feng Yan, Danmei Xu, Moutasem Akkad, Zhewei Yao, Yuxiong He
| Challenge: | Prior Diversify-then-Verify pipelines generate interpretations and then retrieve evidence . ambiguous queries require RAG to disambiguate into interpretations that can be answered from corpus . |
| Approach: | They propose a novel approach that unifies diversification with verification by integrating retriever relevance and generator answerability feedback early. |
| Outcome: | The proposed approach improves grounding-aware F1 by 23% over baselines across multiple LLMs. |
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. |
Normalizing Mutual Information for Robust Adaptive Training for Translation (2022.emnlp-main)
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| Challenge: | Neural machine translation models have been reported to generate hallucinations . despite the success of the models, there are still challenges to improve fluency . |
| Approach: | They propose a scoring metric for the importance of target sentences and tokens to encourage fluent translations. |
| Outcome: | The proposed metric improves translation fluency and source-faithfulness . the proposed nmi model is not properly normalized, the authors argue . |
Inference Scaling for Bridging Retrieval and Augmented Generation (2025.findings-naacl)
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| Challenge: | Existing work observed the generator bias, such that improving the retrieval results may negatively affect the outcome. |
| Approach: | They propose to use inference scaling to aggregate inference calls from the permuted order of retrieved contexts to create a new ranking. |
| Outcome: | The proposed approach improves ROUGE-L on MS MARCO and EM on HotpotQA benchmarks by 7 points. |
RoToR: Towards More Reliable Responses for Order-Invariant Inputs (2025.acl-long)
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| Challenge: | Existing solutions to positional bias in listwise inputs are limited on practical problems . e.g., lost-in-the-middle problem is a common problem for listwise models . |
| Approach: | They propose a zero-shot order-invariant LM for order- invariant inputs with minimal modifications of positional IDs and Selective Routing for listwise tasks. |
| Outcome: | The proposed framework can handle order-invariant and sensitive inputs in listwise tasks. |
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. |