Papers by Yufan Huang
Skeleton-Guided-Translation: A Benchmarking Framework for Code Repository Translation with Fine-Grained Quality Evaluation (2025.findings-emnlp)
Copied to clipboard
Xing Zhang, Jiaheng Wen, Fangkai Yang, Yu Kang, Pu Zhao, Junhao Wang, Maoquan Wang, Yufan Huang, Shengyu Fu, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing code translation benchmarks focus on individual functions, overlooking repository-level challenges like intermodule coherence and dependency management. |
| Approach: | They propose a framework for benchmarking Java-to-C# translation at the repository level . it uses a translation framework guided by skeletons and fine-grained quality evaluation . |
| Outcome: | The proposed framework improves Java-to-C# translation quality at the repository level. |
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)
Copied to clipboard
Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository. |
| Approach: | They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference. |
| Outcome: | The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement. |
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
Copied to clipboard
| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
| Outcome: | The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. |
Continual Learning for Text Classification with Information Disentanglement Based Regularization (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing continual learning methods focus on preserving knowledge from previous tasks . Continual learning is a useful tool for learning over time, but it is not always possible to generalize to new tasks. |
| Approach: | They propose a disentanglement-based regularization method for continual learning on text classification that disentangles text hidden spaces into generic representations and regularizes them differently to constrain knowledge required to generalize. |
| Outcome: | The proposed method disentangles text hidden spaces into representations that are generic to all tasks and representations specific to each individual task. |
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)
Copied to clipboard
Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang
| Challenge: | Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios. |
| Approach: | They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts. |
| Outcome: | The proposed model performs comparable to state-of-the-art large models on the test set. |
Program Translation via Code Distillation (2023.emnlp-main)
Copied to clipboard
| Challenge: | Software version migration and program translation are costly parts of the lifecycle of large codebases. |
| Approach: | They propose a model that captures semantic and structural equivalence of code in a language agnostic intermediate representation. |
| Outcome: | The proposed model achieves state-of-the-art performance on CodeXGLUE and TransCoder GeeksForGeeks translation benchmarks. |
Navigating the Dual Facets: A Comprehensive Evaluation of Sequential Memory Editing in Large Language Models (2024.acl-long)
Copied to clipboard
Zihao Lin, Mohammad Beigi, Hongxuan Li, Yufan Zhou, Yuxiang Zhang, Qifan Wang, Wenpeng Yin, Lifu Huang
| Challenge: | Memory Editing (ME) has emerged as an efficient method to modify erroneous facts or inject new knowledge into Large Language Models (LLMs). |
| Approach: | They propose to evaluate LLMs with single edit only and parameter-modifying ME with parameter-preserving ME. |
| Outcome: | The proposed method can maintain LLMs’ fundamental capabilities but struggles to accurately recall edited knowledge presented in a different format. |
SUT: Active Defects Probing for Transcompiler Models (2023.emnlp-main)
Copied to clipboard
Mengnan Qi, Yufan Huang, Maoquan Wang, Yongqiang Yao, Zihan Liu, Bin Gu, Colin Clement, Neel Sundaresan
| Challenge: | Existing datasets are often criticized for their lack of granularity, which can mask deficiencies in basic syntactic elements that humans care about. |
| Approach: | They propose a new program translation metrics that address basic syntax errors . they propose BLUE, CodeBLUE and computation accuracy metrics which address these errors based on a highly interpretable evaluation harness. |
| Outcome: | The proposed model passes the unit tests with a 26.15% pass rate compared to previous models . |
Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)
Copied to clipboard
Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang
| Challenge: | Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations. |
| Approach: | They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision. |
| Outcome: | The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant. |
Process-Supervised Reinforcement Learning for Code Generation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing reinforcement learning strategies based on outcome supervision have shown effectiveness in code generation tasks, but their effectiveness in the field of code generation remains limited. |
| Approach: | They propose a method that uses a teacher model to mutate and refactor statements and a compiler to automatically label them. |
| Outcome: | The proposed method improves performance in complex code generation tasks. |
MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)
Copied to clipboard
Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi
| Challenge: | Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities. |
| Approach: | They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS. |
| Outcome: | The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution . |