Papers by Tengfei Wen
CodeRise: Bootstrapping LLMs for Ultra Low-Resource Programming Languages via Progressive Self-Refinement Curriculum (2026.findings-acl)
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| Challenge: | Existing methods for training data generation for low-resource languages suffer from a cold-start problem and lack diversity. |
| Approach: | They propose a two-stage framework that generates a high-quality, diverse, and progressively complex curriculum for Ultra Low-Resource Programming Languages (ULRPLs) they leverage the full formal syntax of the target language as structural guidance and apply a biased sampling strategy over library modules. |
| Outcome: | The proposed framework outperforms training-free and training-based baselines on two ULRPLs, Tengo and Janet. |
Code-SPA: Style Preference Alignment to Large Language Models for Effective and Robust Code Debugging (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in coding tasks like code generation and debugging. |
| Approach: | They propose a method which aligns noisy code with the well-structured style familiar to LLMs, mitigating the impact of stylistic inconsistencies. |
| Outcome: | The proposed method improves debugging performance on poorly styled code across the HumanEval, MBPP and EvalPlus datasets. |
Analyze, Generate and Refine: Query Expansion with LLMs for Zero-Shot Open-Domain QA (2024.findings-acl)
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| Challenge: | Existing methods like GAR and EAR rely heavily on supervised training and struggle to maintain effectiveness across domains and datasets. |
| Approach: | They propose a QE approach based on a three-step prompting strategy to enhance query expansion by broadening the scope of queries with additional relevant texts. |
| Outcome: | The proposed approach outperforms state-of-the-art methods in out-domain zero-shot scenarios and outperformed existing methods in end-to-end evaluations. |